Home

PDF Manual ATCOR3

1. Panel of Image Based Shadows 1 a a a a Panel of DEM smoothing vedas bob ee a Topographic correction only no atmospheric correction 284 4 The Atm Correction Menu 2 aa a a ATCOR panel EINER Panel tor DEN SS omiso eR ee Ee ROR A Panel to make a decision in case of a DEM with steps Influence of DEM artifacts on the solar illumination image ACI LAICO lt gt eee eee eee eRe Ret ewe Pee eRe Ss Radiometric calibration target specification panel 0000 Radiometric CALIBRATION module 0 0 0 0 00 000000058 Normalized histogram of unscaled shadow function 008 Panel to define the parameters for interactive de shadowing Juicklook of de shadowing results e Image processing options Right panel appears if a cirrus band exists Emissivity selection panel 2 6 0 ee ee Options for haze processing so aoo e a e a a Reflectance ratio panel for dark reference pixels Incidence BRDF compensation panel e e Value added panel for a flat terrain 2 Value added panel for a rugged terrain oaoa LEAL PPT pel rrr a i rn ea a aa JOD Status WIM r s er da RR SRK we Ree EEEE EEE AG eH EES ALCOR Tiled Procosting ogee ein doe ee ee ee ee oe ee Be 66 18 82 LIST OF FIGURES 9 9 01 9 92 9 09 5 54 9 00 9 06 5 57 5 58
2. 2 10 1 4 Spectral solar flux reflected surface radiance 0 10 1 5 Thermal spectral region a 10 2 Masks for haze cloud water show DS QUES TOTS e ek a as Re RR wR RAG eee 104 Standard atmospheric condong sae ee RED eR ES ew ed 10 4 1 Constant visibility aerosol and atmospheric water vapor 10 4 2 Aerosol retrieval and visibility map 2 00000 pees 164 3 Water vapor retrieval ew ewe ew wee a 10 5 Nomstandard conditions gt ss wee ww Oe Ew HE we Be wD HIS Hire removal on oe boo ESR Ow eRe RD owe ee AA 10 5 2 Haze removal method 1 ka kee hb ESE ES ow OE eee ae 10 5 3 Haze removal method 2 e 10 5 4 Haze or sun glint removal over water WILD Lats Pema o ER eee ee eee ee a he eS 10 5 6 De shadowing with matched filter 2 002 008 10 6 Correction of BRDF effects 2 20 e 10 5 1 Nadir normalization Method 5 664484 serrati HES BE wo EDS 10 6 2 Empirical incidence BRDF correction in rugged terrain 10 6 3 BRDF effect correction BREFCOR 2 004 10 6 4 BRD cover index sw i enw se eee Ree ee ERED HD Oe Eee HEH ES 10 7 Summary of atmospheric correction Steps soosoo oeo e a a a 10 7 1 Algorithm for fat terrain ss so orense 10 7 2 Algorithm for rugged terrain a a 10 8 Accuracy of the method escocesas ss ed References A Altitude Profile of Standard Atmosphe
3. For convenience a log and an ini file are created for the documentation of the processing parameters e g datal image_toarad ini In addition the corresponding sensor calibration file will be created Example sensor casi96 scalef 1000 then file casi96_scalef1000 cal will be created on the atcor4 sensor casi96 directory with the radiometric calibration coefficient c1 0 001 for each band Chapter 9 Implementation Reference and Sensor Specifics This chapter discusses miscellaneous topics associated with the current implementation of ATCOR First the user is acquainted with the structure and handling of the atmospheric database Second the supported input output file types are given The next item discusses the preference parameters e g the definition of thresholds employed for the masking of cloud and water areas and options for interpolating certain spectral regions Then the parameters of the inn file are described which is employed for the interactive and batch processing Last but not least a section on problems and tips is included 9 1 The Monochromatic atmospheric database This section presents the technical details of the atmospheric database To be capable of handling typical hyperspectral sensors with arbitrary spectral bands in the so lar and thermal spectral regions a large database of atmospheric LUTs was compiled with the MODTRAN 5 radiative transfer code
4. Image Based Shadows TEM Smoothing Quick Topographic no Atm Correction Figure 5 19 Topographic modules 5 3 1 DEM Import Import Global DEM Import Global Lat Lon DEM from SRTM data to UTM WGS 84 coordinates The approx 200m 0 5 arc min resolution global DEM data is available for download through the help menu Inputs Longitude range range in longitude direction pixel edges in decimal degrees only Latidude range range in latitude direction pixel edges deg Output Resolution output pixel size default 200m Output DEM name of DEM x _ele bsq to be written Restrictions The DEM named DEM_SRTM30_WORLD DEM has to be available in the demo_data directory of the ATCOR installation Output converted DEM as ENVI file in UTM coordinates Import Geo TIFF This function import a Geo Tiff file to an ENVI formatted elevation data file Import ARC GRID This Procedure reads a standard ARC GRID digitial elevation model CHAPTER 5 DESCRIPTION OF MODULES 68 000 x Import Global DEM Data from src_idlratcor atcor_4demo_dataDEM_SRTM30_WORLD DEH Longitude range from 10 8000 to 11 8000 Latitude range from laz 6200 tot 48 6200 Output Resolution m 20 000 Help Create DEH Done Figure 5 20 Import DEM from global elevation data SRTM Inputs Filename Name of DEM to be read usually extension asc Default output value for found missing data Value to be written to the pix
5. eq 8 2 and they are documented in the corresponding log file created by program HS2MS 7 1 k Ris A L ROAD j l 8 2 Fig 8 2 describes the sequence of processing for the sensor simulation in the solar region After atmospheric correction with ATCOR the image_atm bsq contains the surface reflectance cube Program TOARAD then calculates the at sensor radiance for a different solar geometry or at mospheric parameters All parameters not specified as keywords see list of keywords below are taken from the zmage inn file created by ATCOR The program HS2MS can be started to resample the radiance cube to a ms image by specifying the ms sensor i e channel filter functions and the ms noise equivalent radiance NER NER O is allowed so the resampled image product will only include the noise of the hs scene which will be reduced due to the inherent integration over several hs bands A channel constant NER or a file with channel dependent NER values may also be employed Figure 8 3 shows the GUI panel of program HS2MS Although the input image will usually be a hyperspectral scene with n gt 50 channels and the output a multispectral scene with m lt lt n channels this program can also be employed for the case of a multispectral input image with n lt 10 channels and a panchromatic sensor with m 1 band The program supports the four cases of resampling mentioned above i
6. Arbitrary Actions New Sensor A new sensor is created within the ATCOR installation which results in a new directory in the sensor directory of the installation Delete Allows to delete any sensor directory and all of its contents Update Sensor The sensor parameters of the selected sensor definition are updated according to the settings Rename The current sensor is renamed both directory and sensorx dat file Outputs A new sensor_x dat file and possibly sensor directory is created ATTENTION this routine requires write access to the sensor directory of the ATCOR installation 5 2 2 Generate Spectral Filter Functions ATCOR requires the spectral response of all bands of a sensor being present as a spectral filter file response file rsp For spectroscopic instruments the band characteristics are often only available by band center and width at FWHM Full width half maximum This function creates the response curves from the latter information compare Fig 5 15 CHAPTER 5 DESCRIPTION OF MODULES 63 Inputs Wavelength File An ENVI header file standard hdr or a wavelength reference file Format of ASCII File 3 columns no header column 1 band number column2 center wavelength column 3 band width Unit nm or ym same for columns 2 and 3 Note if the FWHM is not contained in the ENVI header or in the ASCII file a bandwidth resolution is assumed which corresponds to 1 2 time
7. a tropical atmosphere with wv 4 1 cm compare chapter 6 Most multispectral satellite sensors do not have spectral bands placed in water vapor regions so the retrieved reflectance signature will show a rather small dependence on the water vapor estimate For sensors with water vapor bands e g Hyperion or MOS B the retrieved reflectance spectrum strongly depends on the water vapor content In this case the water vapor map can be derived during the image processing see the panel of figure 5 31 as part of the reflectance retrieval Note that the water vapor is not retrieved from the image pixel for correction in the SPECTRA module as it is done in the image processing for hyperspectral systems The button Save last spectrum upper right corner of figure 5 35 can be used to save the selected surface reflectance spectrum A sequence of target DN spectra can also be generated here that is required as input to the spectral calibration module currently only available for the hyperspectral add on module Up to 9 targets can be defined to be used in the spectral calibration They have to be labeled consecutively e g target1 target2 etc These output file names without file name extension have to be specified after pressing the button Save last spectrum For each target name three files will be generated e g target1 dat surface reflectance spectrum targetl tut a description file and targ
8. CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 172 9 6 3 Landsat 8 TIRS Landsat 8 has the TIRS sensor with two thermal channels B10 B11 with center wavelengths near 10 9 12 1 wm respectively The pixel size of the data is resampled to 30 m to match the pixel size Of the OLI data ATCOR allows the calculation of the surface temperature using the split window technique in combination or separately from the OLI bands The B10 B11 data digital number DN is first converted into TOA radiance L using the calibration in the meta file i e L co c DN Next the TOA radiance L is converted into TOA blackbody temperature see http landsat usgs gov Landsat8 Using Product php In ky L 1 where k1 k2 for B10 B11 are included in the meta file The employed split window equation for the approximate calculation of the surface temperature TT is Lop 9 7 Ts Tobio 2 Torio Tbb11 AR tof fset 9 8 where Toffse is an optional user specified offset which is O if not specified The equation with Tof fset 0 holds for a surface emissivity of 0 98 in both TIRS bands which is typical for vegetation Figure 9 5 shows the corresponding surface temperature error for the mid latitude summer MS and US standard atmospheres with the ground at sea level and 0 5 km above sea level The sea level air temperatures for the MS and US atmospheres are 294 K and 288 K respectively The three curves correspond to surface te
9. Depending on processing date the effectiveBandwidth AA um unit is also included The nominal offset is co 0 in each channel and the ATCOR gain c has to be specified in the unit Wem7 sr twm which requires the following conversion equation for Quickbird absCalFactor x 0 1 le A Therefore the template calibration file has to be updated i e copied to a new file name and edited according to the absCalFactor of the scene IMD file and the above spectral bandwidth values AA 9 6 9 IRS 1C 1D Liss The metadata file contains the geographic coordinates as well as the solar elevation and azimuth angles It also includes the radiometric calibration coefficients the bias B Lmin and gain G Lmax in the ATCOR radiance unit mWem sr um The radiometric coefficients for ATCOR s cal file have to be calculated as e co Lmin and cy Lmax Lmin 255 9 6 10 IRS P6 The IRS P6 platform carries three optical sensors the AWiF S advanced wide field of view sensor the Liss 3 and the Liss 4 AWiFS 60 m resolution and Liss 3 20 m have the same spectral bands green red NIR and SWIR1 at 1 6 um the LISS 4 red band serves as the high resolution camera 5 m Similar to the IRS 1C 1D the radiometric calibration coefficients are included in the meta file the bias B Lmin and gain G Lmaz are specified in the unit mWem sr tywm and the CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECI
10. SCS C a modified sun canopy sensor topographic correction in forested terrain IEEE Trans Geoscience and Remote Sensing Vol 43 2148 2159 2005 Ll 98 Sutherland R A Broadband and spectral emissivities 2 18 um of some natural soils and vegetation Journal of Atmospheric and Oceanic Technology Vol 3 199 202 1986 99 Teillet P M Guindon B and Goodenough D G On the slope aspect corrextion of mul tispectral scanner data Canadian J Remote Sensing Vol 8 84 106 1982 100 Wanner W A H Strahler B Hu P Lewis J P Muller X Li C L B Schaaf and M J Barnsle Global retrieval of bidirectional reflectance and albedo over land from EOS MODIS and MISR data Theory and algorithm J Geophys Res vol 102 no 14 pp 17143D17161 1997 1101 Wiegand C L Gerbermann A H Gallo K P Blad B L and Dusek D Multisite analyses of spectral biophysical data for corn Remote Sensing of Environment Vol 33 1 16 1990 1102 Wiegand C L Richardson A J Escobar D E and Gerbermann A H Vegetation indices in crop assessments Remote Sensing of Environment Vol 35 105 119 1991 1103 Wolfe W L and Zissis G J The Infrared Handbook Office of Naval Research Wash ington DC 1985 1104 Young S J Johnson B R and Hackwell J A An in scene method for atmospheric compensation of thermal hyperspectral
11. vis zen ele alti chlist results spline zeroborder 0 1 2 range splitband overwrite Smile detection routine PARAMETERS incube input data cube dbfile raw database file to be used for convolution no height interpolation respfile response file e g band001 rsp resol internal resolution for the calculation outfile name of output file for smile coefficients KEYWORDS featureflags bytarr n_feat 15 feature regions featureflags i 1 if feature is set else 0 vis visibility km zen solar zenith angle deg zenith at 0 deg ele average ground elevation km alti flight altitude elevation km for pressure compensation chlist list of bands which are used for smile detection and for interpolation of the results numbering starting at 0 results write idl save dump of all results in a file named sav together with the regular output spline 1 spline channel interpolation 0 linear channel interpolation of smile coefficients zeroborder 2 set smile coefficients to 0 at spectral borders first last channel 1 repeat smile coefficients outside of interpolated values range search range default 20 nm splitband splitchannel index between two detectors starting at 0 first band of second detector overwrite silently overwrites the older output e at_smoothdem infile dist outfile median DEM smoothing routine PARAMETERS infile input data cube single band ENVI
12. Figure 5 71 Example of classification with SPECL Left true color image of Landsat TM right result of classification CHAPTER 5 DESCRIPTION OF MODULES 113 5 8 5 Spectral Smile Detection This routine uses sharp atmospheric absorption features and Fraunhofer lines for inflight smile i e spectral across track non uniformity detection The calculation is done by correlation analysis of a number of spectral bands in the vicinity of selected absorption features The outputs may be used for smile aware atmospheric correction Initially the smile characterization for each spectrometer channel is derived from laboratory mea surements From such data the wavelength shift with respect to the center pixel of the detector array can be parametrized using a 4th order polynomial fit However in case of instrument changes during the mission a spectral re calibration might be necessary from the image data or from on board calibration facilities using well defined absorption features Onboard spectral calibration devices such as interference or rare earth filters would be well suited for this purpose However such devices are often not available in sensor systems Therefore atmospheric gas absorption features or solar Fraunhofer lines have to be taken as a reference from the imagery itself The processing steps are 1 A calibrated image is averaged in along track direction leading to a signature image of the size of the detector array
13. Int J Remote Sensing Vol 26 3137 3148 2005 73 Richter R Schl pfer D and M ller A An automatic atmospheric correction algorithm for visible NIR imagery Int J Remote Sensing Vol 27 2077 2085 2006 Ll 174 Richter R Bachmann M Dorigo W Mueller A Influence of the adjacency effect on ground reflectance measurements IEEE Geoscience Remote Sensing Letters Vol 3 565 569 2006 References 248 75 Richter R and Schl pfer D Considerations on water vapor and surface reflectance re trievals for a spaceborne imaging spectrometer IEEE Trans Geoscience Remote Sensing Vol 46 1958 1966 2008 176 Richter R Kellenberger T and Kaufmann H Comparison of topographic correction methods Remote Sensing Vol 1 184 196 2009 77 Richter R and D Schl pfer Atmospheric topographic correction for satellite imagery ATCOR 2 3 User Guide DLR IB 565 01 13 Wessling Germany 2013 78 Richter R and D Schl pfer Atmospheric topographic correction for airborne imagery ATCOR 4 User Guide DLR IB 565 02 13 Wessling Germany 2013 179 Richter R Schl pfer D and M ller A Operational atmospheric correction for imaging spectrometers accounting the smile effect IEEE Trans Geoscience Remote Sensing Vol 49 1772 1780 2011 80 Richter R Wang X Bachmann M and Schl pfer D Correction of cirrus effects
14. raflactance YMIN p o 0 0 YMAX ER Clear screen Bret T C las 4 raflactance Create Zoom Window Contrast stretching Gaussian w Histo Eq Return LS wavelength um YMIN p o YMAX 20 0 Clear screen 2 ate U ee 35 5 Figure 5 35 SPECTRA module To obtain a target spectrum of the scene click at any position in the image In figure 5 35 the solid white line spectrum at the top shows a coniferous forest signature the green line represents a spruce reference spectrum taken from the spec_lib directory already resampled for the Landsat 5 TM sensor The symbols mark the TM center wavelengths A reference spectrum can be loaded when clicking the corresponding button at the top right of the panel The bottom graphics shows soil and water spectra taken from the scene and a soil reference spectrum from the spec_lib library CHAPTER 5 DESCRIPTION OF MODULES 81 An exact match of scene spectra and library spectra cannot be expected but the trends spectral shapes should be consistent The parameter Visibility can be set in the corresponding widget near the center of the panel and its influence on the retrieved spectrum will be shown when placing the target box in the scene To vary the atmospheric water vapor content the user should switch to the main panel and select a different atmosphere e g switch from the mid latitude summer atmosphere with a column wv 2 9 cm to
15. So if the desired reflectance reduction factor is G then the required threshold angle can be calculated from eq 10 123 with b 1 10 127 COS Br arccos G In many cases a separate treatment of BRDF effects for soil rock and vegetation provides better results For this purpose two modes of BRDF correction are available for vegetation compare the graphical user interface of Figure5 45 The first mode is superior in most cases Reference 76 contains a comparison of different topographic correction methods for several Landsat TM ETM and SPOT 5 scenes from different areas The proposed empirical ATCOR approach performed best in most of these cases but no method ranked first in all cases 10 6 3 BRDF effect correction BREFCOR A generic BRDF effects correction routine BREFCOR 90 has been included in ATCOR The idea is to apply a scaling of the volume scattering and the geometric scattering component within a well accepted BRDF model A fuzzy surface cover index of the complete image image is used for this purpose which covers all surface types from water to asphalt and concrete soils sparse vegetation and dense vegetation The Ross Li sparce reciprocal BRDF model has been selected as basis for the correction of re flectance anisotropy 35 This model is mainly developed for vegetation but we use it in a scaled way for all kind of surfaces Literature mainly related to MODIS atmospheric correction routines
16. Step 3 calculates the covariance matrix C p where p is the surface reflectance vector comprising only the non water and non cloud pixels For each pixel this vector holds the reflectance values in the 3 selected channels around 0 85 1 6 2 2 ym The matched filter is a vector tuned to a certain target reflectance spectrum p to be detected 2 CT pt P ps P C1 pt P Vnt 10 113 _unscaled shadow function gt threshold core shadow areas gt expand shadow mask gt scene lt gt Figure 10 22 Flow chart of processing steps during de shadowing Here p is the scene average spectrum without the water cloud pixels Selecting p 0 for a shadow target yields a special simplified form of the matched filter where the sh index symbolizes shadow C lp eee e sh pi C 1p 10 114 CHAPTER 10 THEORETICAL BACKGROUND 225 The shadow matched filter vector is then applied to the non water non cloud part of the scene and yields the still un normalized values that are a relative measure of the fractional direct illumination also called unscaled shadow function here x y Voy p 1 y P 10 115 The matched filter calculates a minimum RMS shadow target abundance for the entire non water non cloud scene Therefore the values of are positive and negative numbers The arbi trary image depending range of has to be rescaled to the physical range from 0 to
17. b b gq where g is a user defined lower cut off value of 6 exponent bt see below se Al G GL with exponent b 1 0 linear BI G GL with exponent bel sort recommended standard BRIF start angle it degree 60 0 g smallest value of G range 0 1 1 1 250 BRIF correction for vegetation al weak correction in NIR SWIR superior in most cases b stronger correction in NIR SWIR lambda gt 720 nm se Lal G GL with b 3 4 lambda lt 720 nm b 1 3 for lambda gt 720 hm bi G Gl with b 34 lambda lt 720 nm b 1 0 for lambda gt 720 nm ee Tone Figure 5 45 Incidence BRDF compensation panel Figures 5 46 and 5 47 are associated with the value added products as described in chapter 7 This value added file contains up to 10 channels if the sensor has thermal bands In case of a flat terrain the air temperature has to be specified For a rugged terrain air temperature at some base elevation and the air temperature gradient as well as water vapor have to be defined If the value added option is selected another panel pops up Figure 5 48 It contains parameters for the leaf area index LAI model and FPAR model as described in chapter 7 Finally a job status window indicates the processing progress Note The job status window of ATCOR shows the percentage of processed image data and the estimated remaining time The time estimate is based on the processing time for the current
18. 0 average vis index of reference areas is employed for non reference pixels itriang 1 triangular interpolation of visibility index of reference areas ratio_red_swir ratio of surface reflectance of red to 2 2 um band for the reference pixels If no 2 2 um band exists but a 1 6 wm band the ratio holds for the red to 1 6 um band If only VNIR bands exist 400 1000 nm the ratio holds for the red to NIR band ratio_blu_red ratio of surface reflectance of blue band to red band for the reference pixels line 22 0 65 0 0 25 ibrdf beta_thr thr_g parameters for BRDF correction in rugged terrain For a flat terrain these parameters are not used e ibrdf 0 no empirical BRDF correction or flat terrain e ibrdf 1 correction with cosine of local solar zenith angle eq 10 123 with b 1 e ibrdf 2 correction with sqrt cos of local solar zenith angle eq 10 123 with b 1 2 e ibrdf 11 correction with cosine of local solar zenith angle eq 10 123 with b 1 for soil sand Vegetation eq 10 123 with exponent b 3 4 and b 1 3 for A lt 720 nm and A gt 720 nm respectively i e option a in the BRDF panel see Figure 5 45 weak correction e ibrdf 12 correction with cosine of local solar zenith angle eq 10 123 with b 1 for soil sand Vegetation eq 10 123 with exponent b 3 4 and b 1 for A lt 720 nm and A gt 720 nm respectively i e option b in the BRDF panel see Figure 5 45 stron
19. Eg z global flux direct plus diffuse solar flux on a horizontal surf at elevation z Elz radiation incident upon adjacent slopes pan 0 1 initial value of average terrain reflectance De vin 2 y locally varying average terrain reflectance calculated iteratively i 1 2 3 Verran 00 terrain view factor range 0 1 The solar and DEM geometry is shown in figure 10 6 as well as the three solar radiation components taken into account for rugged terrain direct and circumsolar irradiance and diffuse hemispherical sky flux It can be shown that these three components are equivalent to the direct and diffuse solar flux components in flat terrain In case of a shadow pixel the direct and circumsolar components CHAPTER 10 THEORETICAL BACKGROUND 190 Sensor Fa Fi Pd Sky and terrain view factors ig Ada corey Aeration A Rel Terrain Fediation trigonometric approach Man m 1 Tar horizon line approach Adjacessy Neighbourhood 1 26 Figure 10 5 Radiation components in rugged terrain sky view factor Left schematic sketch of radiation components in rugged terrain 1 path radiance 2 pixel reflected radiance 3 adjacency radiance 4 reflected terrain radiance Right sky and terrain view factor are set to zero i e the binary factor b 0 The next step iterates eq 10 15 averaging the reflected terrain radiation over a square box of 1 0 x 1 0 km If equation 10 15 is used with E E then
20. F Include Terrain Illumination Calculate Skyview Estimate Options Apply Shade Pixel Filter Write all Side Layers Range of index from full cast shadow to none Maxi 2 0 1 300000 tot 1 500000 Select Slope File Names Ycubes ads _temp Tuellec101409512013_876 swissalti_25_95_e _DTM_1022_6_0_slp bsq Solar Zenith deg 25 9000 Solar Azimuth deg 155 800 Aircraft Altitude ag km 5 18300 Define Name of Output Illumination File oubes ads _tenp Tuellec101408512013_876 201208151 022NRGBNOOMLZOBL2_0_0_ilu bsq Help Run Done E ES Figure 5 27 Panel of Image Based Shadows e Apply Shade Pixel Filter filters single pixels within full cast shadow areas e Write all Side Layers creates a file containing all indices calculated during the processing in 5 7 layers This function may be useful to find the appropriate range limits for a sensor system or imaging situation e Limits of Index The lower and upper boundary can be set in order to derive a continuous fractional shadow from index Larger values will result in large fractions of shadows being detected The range can not exceed 0 to 2 in any case default values are 0 5 to 1 Parameters used for terrain illumination and skyview estimate e Slope File Name Name of input Slope file to be used for illumination calculation the corre sponding _asp bsq file needs also to be present e Solar Zenith
21. Ly co 41 DNf Lo co C1 DN 2 14 This can be performed with the co amp c option of ATCOR s calibration module see chapter 5 The result is Li La DN DN 2 15 Co Ly C DN 2 16 Equation 2 15 shows that DNf must be different from DNS to get a valid solution i e the two targets must have different surface reflectances in each band If the denominator of eq 2 15 is zero ATCOR will put in a 1 and continue In that case the calibration is not valid for this band The requirement of a dark and a bright target in all channels cannot always be met Calibration with n gt 2 targets In cases where n gt 2 targets are available the calibration coefficients can be calculated with a least squares fit applied to a linear regression equation see figure 2 4 This is done by the cal_regress program of ATCOR It employs the rdn files obtained during the single target calibration the cl option of ATCOR s calibration module See section 5 8 7 for details about how to use this routine Note If several calibration targets are employed care should be taken to select targets without spectral intersections since calibration values at intersection bands are not reliable If intersections of spectra cannot be avoided a larger number of spectra should be used if possible to increase the reliability of the calibration 2 4 De shadowing Remotely sensed optical imagery of the Earth s
22. Select Sensor Spectral Responset Yerc_idl atcor atcor_4 sensor APEX_2015 001 rsp ee K Sensor Calibration Pressure src_id atcor atcor_4 sensor APEX_2015 pressure dat POE Define Output Smile Coefficients Ycubes apex smile_poly_ord4 dat Show Detection Resolution nm 1 050000 Search Range nm factor E Band Range p to E Split Band 104 Visibility km 0 0000 Solar Zenith deg 42 0000 Mean Ground Elevation km 542000 Flight Altitude km 7 06000 Feature llavelengths rm il E 430 E 486 E 527 MW 586 M 686 M 760 MW 820 MW 940 M 1130 F 1268 F 1470 F 1572 F 2004 F 2055 F 2200 F 2317 F 2420 Spectral Interpolation Type Linear w Spline Extrapolation Type to detector limits w extrapolate trend repeat values w to zero at borders Help Detect Smile Detect FWHM Plot Save Report Done Figure 5 72 Spectral smile detection CHAPTER 5 DESCRIPTION OF MODULES 116 5 8 6 Spectral Calibration Atm Absorption Features The program SPECTRAL_CAL is only intended for hyperspectral sensors and employs atmo spheric absorption features to detect and remove possible wavelength calibration errors see chapter 2 2 For this purpose a certain number of target spectra have to be selected in the SPECTRA module NOTE Alternatively to this routine the spectral calibration can be done by first using the spectral smile detection routine see section 5 8 5 and then applying the shift
23. and to the large haze mask if HOT gt mean HOT 0 5 o HOT Then the HOT histogram of all haze pixels is calculated Pixels with values less than 40 of the cumulative histogram are assigned to thin medium haze pixels with higher values to medium thick haze This distinction is arbitrary and has no effect on the subsequent processing Haze over water Pixels must belong to the water mask and the NIR apparent reflectance p N IR must be greater than the NIR clear water threshold Telear aterw1r defined in the preference parameter file chapter 9 3 Thin haze over water is defined as T clearwater y IR 2 p NIR gt 0 06 10 63 Medium haze over water is defined as 0 06 lt NIR lt T gt 10 64 where To default 0 12 is another editable parameter in the preference file The method of haze removal over water is described in chapter 10 5 4 The same technique is also employed to remove sun glint 10 3 Quality layers The previous section defined a coarse pixel classification which is useful for an atmospheric cor rection In addition it supports an assessment of the quality of the processing For example a large error in the radiometric calibration could cause a scene classification with all pixels labeled as water In this case a user can immediately identify the problem Of course a more detailed assessment is possible with an analysis of the reflectance spectra Nevertheless the classification map la
24. istretch_type 1 linear stretching 2 exponential stretching of Y into line 29 ch940 1 6 vector with 6 channel numbers for the 940 nm water vapor retrieval ch940 1 left window channel 850 890 nm ch940 2 right window channel 850 890 nm ch940 3 left absorption channel 920 970 nm ch940 4 right absorption channel 920 970 nm ch940 5 left window channel 1000 1040 nm ch940 6 right window channel 1000 1040 nm The left and right channel numbers for each window or absorption region may be the same Put in a zero channel number if not applicable If the 820 nm water vapor region is selected the vector ch940 holds the corresponding window and absorption channels for this region line 30 ch1130 1 6 vector with 6 channel numbers for the 1130 nm water vapor retrieval ch1130 1 left window channel 1050 1090 nm ch1130 2 right window channel 1050 1090 nm ch1130 3 left absorption channel 1110 1155 nm CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 167 ch1130 4 right absorption channel 1110 1155 nm ch1130 5 left window channel 1200 1250 nm ch1130 6 right window channel 1200 1250 nm The left and right channel numbers for each window or absorption region may be the same Put in a zero channel number if not applicable line 31 chth_w1 chth_al chth_a2 chth_w2 bands for thermal water vapor retrieval 10 12 wm chth_w1 left window channel SW
25. 2 The surface reflectance is calculated atmospheric correction and smoothed 3 The spectral bands within the spectral matching range are selected 4 Spectral shifts with intervals between 0 01 0 05 nm are calculated and applied to the selected spectral band response functions 5 An appropriate pre calculated fine spectral resolution atmospheric LUT is selected which serves for the calculation of at sensor radiance values for the series of spectrally shifted re sponse functions using the surface reflectance spectrum from step 2 6 The derived spectral signatures are then correlated to the observed column averaged signal in the image such that the best fitting spectral shift AA A can be found for each image column j i e the A with the highest Pearson s correlation coefficient is selected This is equivalent to minimizing the merit function Ak HNM A X Er j k ErOg Aj k 5 7 Aj Ap 5nm where L 3 k is the average at sensor radiance of the image for column j and channel k and Er Ax Aj k is the corresponding reference radiance for a wavelength shift A within a 5 nm interval around Az By using the continuum removed scaled radiances the stability of the routine is enhanced 7 A 4th order polynomial is fitted through the calculated spectral points and the respective polynomial parameters of eq 4 1 are stored 8 The polynomial parameters are interpolated and optionally extrapolated to all other
26. 4 2 4 3 4 4 ret 0 2 9 3 10 1 10 2 A l A 2 A 3 A A A 5 A 6 A 7 Example of a sensor definition file no thermal bands 41 Sensor definition file instrument with thermal bands 42 Sensor definition file smile sensor without thermal bands 43 Class label definition of hew file 2 648484 ei ee AA 45 Heat fluxes for the vegetation and urban model 143 Elevation and tilt angles for Ikonos 0 2 eee 178 Elevation and tilt angles for Quickbird 0 2 2 00 eee eee 179 Radiometric coefficients cl for ASTER 2 0 00 0000 ee eee 180 Class labels in the hew file e 202 Visibility iterations on negative reflectance pixels red NIR bands 209 Altitude profile of the dry atmosphere 00 00 eee ee eae 250 Altitude profile of the midlatitude winter atmosphere 0 251 Altitude profile of the fall autumn atmosphere 204 251 Altitude profile of the 1976 US Standard o aon aoa a a a a 251 Altitude profile of the subarctic summer atmosphere 252 Altitude profile of the midlatitude summer atmosphere 252 Altitude profile of the tropical atmosphere e 252 11 Chapter 1 Introduction The objective of any radiometric correction of airborne and spaceborne imagery of optical sensors is the extr
27. 4 E W Figure 5 25 Example of a DEM left with the corresponding sky view image right DEH File may have 16 or 32 bit integer or Float data Output Shadow File Vauto_as data atcor2 3 deno_data tn_rugged ta_blforest_zend9_aril 7_chd a DVERWRITE Solar zenith segle degree a 45 Solar azimuth angle degree Denorth Miesask etc DEH resolution x 4 pixel size meters Figure 5 26 Panel of Cast Shadow Mask Calculation SHADOW Input Files e infile file to be analysed requires at least 4 bands Blue Green Red NIR e calfile calibration file for current input file usually found in the sensor or cal directory of the ATCOR installation e e0 solar solar irradiance file for current input this file is also situated in the sensor or cal directory e outfile output file of the processing Options e Include Terrain Illumination calculates the illumination based on slope and aspect files and the solar illumination angles as defined below e Calculate Skyview Estimate calculates an estimate of the local skyview fraction on the basis of a linear model CHAPTER 5 DESCRIPTION OF MODULES 13 600 ATCOR Shadow Detection select Input File Name Veubes ads _tenp Tuellec101408512013_876 201 208151 022NRGENOOALZ08L 2_0_0 bsa Calibration Filet erc_id atcor atcor_4 sensor adsi0 ads_standard cal Select Solar Reference File EQ sre_idl atcor atcor_A sensor ads80 e0_solar_ads80 spc
28. 940 and 1130 nm bands are employed e Haze removal is enabled by setting the parameter zhaze gt 0 no haze removal is specified with ihaze 0 Separate parameter values define haze removal over land haze sunglint removal over water and the combination Some criteria exist to check whether the haze land removal is likely to yield good results The haze land algorithm is switched off if those criteria are not passed ihaze 1 However as these criteria might fail in certain cases there is the option of setting 2haze 1 which enforces the haze removal disregarding the termination criteria ihaze 0 no haze correction ihaze 1 haze land correction might be switched off if quality check criteria are not passed ihaze 2 haze over water removal requires clear water pixels ihaze 3 haze land and haze water removal ihaze 4 sun glint removal over water ihaze 1 haze land correction is executed disregarding quality checks ihaze 2 is treated as ihaze 2 ihaze 3 haze land removal is forced haze water removal needs clear water pixels Haze removal is performed for the visible bands sun glint removal for all bands iwat_shd 0 water pixels are excluded from de shadowing land default iwat_shd 1 water pixels are included in de shadowing land The option zwat_shd 1 might be useful if the internal water classification based on purely spectral critera fails and dark land pixels are classified as water which is excluded
29. A2 A3 A1 and W3 A2 A1 A3 A1 10 91 The problem is the estimation of the surface reflectance p2 in the absorption band eq 10 90 The technique tries to estimate the reflectance p2 with a linear interpolation of the surface reflectance values in the window channels ch 1 3 that are not or only slightly influenced by the water vapor content Therefore the reflectance pa is calculated as P2 W1P1 W303 10 92 Then equation 10 90 can be written as Ripa p272 u Fg2 u T2 u Eya u prnlu 0 Eg2 u 0 7 T2 u 0 Eg2 u 0 eee CHAPTER 10 THEORETICAL BACKGROUND 215 Reflectance 0 90 0 95 1 00 Wavelength pm Figure 10 15 Reference and measurement channels for the water vapor method The at sensor radiance is converted into an at sensor reflectance where Ej2 u is the global flux on the ground for the measurement channel index 2 ATCOR employs 4 to 5 water vapor columns u 0 4 1 0 2 0 2 9 4 0 cm sea level to space geometry to calculate an exponential fit function Rappa u exp a Byu 10 94 which can be solved for the water vapor column u see Fig 10 16 where the diamonds in the figure mark the calculated water vapor grid points u 0 4 1 0 2 0 2 9 cm InR ee A 10 95 Equations 10 90 10 92 to 10 95 are iterated starting with u 1 0 cm calculating Rappa up dating u Li u p1 p3 and repeating the cycle A minimum of two channels one reference one measure
30. For the shadow quantification the brightness in the NIR spectral band is first calculated using the solar illumination Secondly two blue indices have been defined as the band ratios green blue and red blue respectively These three measures are combined such that a value equivalent to the illumination between 0 and 1 is created 0 being a cast shadow area The shadow fraction parameter is then defined as Figure 10 7 Combination of illumination map left with cast shadow fraction middle into continuous illumination field right Lre L reen Pshad max 5 Ee 0 35 S 0 42 6 i Popp rir 10 20 blue Lblue T Drea where Loiue Lgreen and Lreg are the at sensor radiance values in the true color bands and papp nir is the apparent at sensor reflectance in the near infrared band The scaling factors here 5 8 and 6 are chosen such that all three parameters are within the same range i e a value range between 0 and 2 and optimized for full cast shadow at 0 5 The parameter Pshad is then scaled to a shadow fraction number fshaa between 0 full cast shadow and 1 no cast shadow using empirically found limits These limits may be variable between sensors In a second step this map is combined with the standard geometrically derived illumination field see Fig 10 7 The resulting illumination map serves as an input to the ATCOR method Skyview Factor Estimate The skyview factor Vs y describes the re
31. L reflected radiance L3 adjacency radiation During the following discussion we will always use eq 2 7 Disregarding the adjacency component we can simplify eq 2 6 L Lpath T Lreflected Loath AN TpE T Co c DN 2 9 where 7 p and E are the ground to sensor atmospheric transmittance surface reflectance and global flux on the ground respectively Solving for the surface reflectance we obtain m d co e1DN L pa MO ADN Lenin 2 10 TEg The factor d takes into account the sun to earth distance d is in astronomical units because the LUT s for path radiance and global flux are calculated for d 1 in ATCOR Equation 2 9 is a key formula to atmospheric correction A number of important conclusions can now be drawn e An accurate radiometric calibration is required i e a knowledge of cy cy in each spectral band e n accurate estimate of the main atmospheric parameters aerosol type visibility or optical thickness and water vapor is necessary because these influence the values of path radiance transmittance and global flux e If the visibility is assumed too low optical thickness too high the path radiance becomes high and this may cause a physically unreasonable negative surface reflectance Therefore dark surfaces of low reflectance and correspondingly low radiance cy c1 DN are especially sensitive in this respect They can be used to estimate the visibility or at least a lower b
32. Sensor Description extension sensor_ type dat Sensor description file as created by the Function File New Sensor Format ASCII This file is only requested for user defined sensors not for the standard sensors Solar Reference extension e0_solar_ type dat Solar reference function for the sensor as created when compiling the atmospheric LUT for a sensor Format ASCII three columns center wavelength bandwidth extraterrestrial solar irradiance 9 2 2 Main output The default output image data type is byte if the input is byte data Then a scale factor s 4 is employed i e the per cent reflectance value of each pixel is multiplied with s 4 and rounded to byte As an example a surface reflectance value of 20 2 will be coded as 81 However the user can modify the scale factor on ATCOR s main panel A value of s 10 to s 100 causes the output file to be coded as signed 16 bit integer i e with two bytes per pixel The specification s 1 0 produces a float output image i e with 4 bytes per pixel Attention The float output could be used for testing on small images For large files and an input data type of 2 bytes per pixel the output float image would require twice the disk space of the input image The default output data type is signed 16 bit integer for all integer and float input data employing the scale factor s 100 The scale factor is always included in the output ENVI header file Note Any positive value of
33. Wm Con Spatial maps files of air temperature and air emissivity can also be included in the processing Usually isolated point like measurements of air temperature are available from meteorological sta tions These have to be interpolated to generate a spatial map coregistered to the image prior to applying the ATCOR model Data in the file containing the air temperature must have the Celsius unit data of the air emissivity file must range between O and 1 Future improvements to the ATCOR model will include an air temperature map derived from the image triangle or trapezoidal method employing the thermal band surface temperature and NDVI Carlson et al 1995 Moran et al 1994 In case of mountainous terrain the air temperature Ta z0 and water vapor partial pressure Pwv zo at a reference elevation zg have to be specified The height dependence of air temperature is then obtained with linear extrapolation employing a user specified adiabatic temperature gradient OT 0z Ta 2 Talo 5 20 2 7 28 where 07 0z is typically in the range 0 65 0 9 Celsius 100 m The water vapor partial pressure is extrapolated exponentially according to Ple Dis a 10 7 29 CHAPTER 7 VALUE ADDED PRODUCTS 144 where zs is the water vapor scale height default 6 3 km The list of all output channels of the value added flx bsq file is 1 2 10 11 Soil adjusted vegetation index SAVI scaled
34. an external source if a file scene_hcw bsq exists in the same folder as the scene bsq then this information is taken and the internal ATCOR calculations for this map are skipped In this case the coding of the surface types has to agree with the ATCOR class label definition of course see Table 10 1 This file is written if the corresponding flag is set to 1 see chapter 9 3 and figure 5 11 in chapter 4 Depending on the available spectral channels it may not be possible to assign certain classes Table 10 1 contains one class for cloud over land meaning water cloud whereas the low optical thick ness cloud is put into the thin and medium thickness haze class T hin and medium haze can often be corrected successfully Of course there is no clear distinction between thick haze and cloud We take a pragmatic view and if the haze removal is successful in areas with thick haze then these pixels can be included in the haze mask Since this is not clear at the beginning it might be necessary to run the program twice with and without haze removal A check of the results will reveal whether the haze removal was successful ATCOR contains a number of criteria to assess the probability of a successful haze removal and will switch off the haze option if the chances are not good This automatic haze termination works in most cases but a success cannot always be guaranteed There are 5 cirrus classes thin medium thick cirrus cloud thic
35. c 0 1 AbsCalFactor FWHM 9 15 where FWHM is the effective bandwidth effectiveBandwidth in wm as specified in the metafile Although the bandwidth is constant per channel the gain c might have to be updated because the absCalFactor can vary from scene to scene Additionally panchromatic images with a 0 5 m resolution are available CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 182 9 6 16 THEOS THEOS is a satellite mission of Thailand containing a multispectral and a panchromatic instru ment The multispectral sensor has 4 channels in the visible near infrared similar to the first 4 bands of Landsat 5 TM but the spatial resolution is 15 m and swath 90 km The panchro matic instrument has a spectral filter curve similar to Landsat 7 ETM panchromatic but the spatial resolution is 2 m and swath The data encoding is 8 bits pixel the sensor has adjustable gain settings documented in the metafile for each scene The gain factor g is given in the unit 1 Wm sr um and it has to be converted into the gain c for ATCOR according to C1 0 1 g 9 16 where the factor 0 1 accounts for the unit mWem sr um The offset co is zero for all channels The metafile specifies the satellite incidence angle which can be used to calculate the off nadir viewing angle ATCOR input using eq 9 9 orbit 826 km The viewing angle can also be calculated from two other angles in the metafile the
36. depends on the available memory for a specific machine because ATCOR performs most calcula tions in memory loading one or two complete bands of the scene A typical tile size for modern machines is 3000 3000 pixels 9 Mpixels to 5000 5000 pixels 25 Mpixels The user has to try and find out the appropiate tile size As an example with a 9 Mpixel tile size and a 30 Mpixel scene the image has to be split into 4 sub scenes Assuming that the number of image columns and lines is approximately the same one would choose the keywords ntx 2 nty 2 in this example Of course processing of much smaller tiles is also possible e g ntx 20 nty 10 but this is not recommended because of potential image border effects i e larger visibility differences for the small tiles might lead to seams at the tile borders The TIFF input data format is supported however channels in the TIFF file must have the in creasing band order and the maximum number of bands should be less than 10 If all channels are in one input TIFF file example image tif the output TIFF after atmospheric correction will also hold all channels example image_atm tif An IDL routine called write_atcor3_inn_file is available to users who want to generate the inn file without the ATCOR GUL Note On the IDL command line the command atcor has to be typed first to load the atcor sav file Then the atcor2_tile or atcor3_til
37. dry veget soil e 0 97 sand asphalt e 0 96 band 13 at 10 661 micron A Normalized Emissivity Method NEM max emissivity specified below Define constant scene emissivity Original NEM all surface types have the same max emissivity Adjusted NEM surface types have different max emissivity Adjusted NEM max emissivity water 9300 Adjusted NEM max emissivity green vegetation 1 200 Adjusted NEM max emissivity dry veget soil b 9750 Adjusted NEM max emissivity asphalt sand 10 9650 ISAC In Scene Atmospheric Compensation 2 ISAC and NEM separate emissivity files _ DONE Figure 5 42 Emissivity selection panel The first two options are available for instruments with only a single thermal band the NEM and CHAPTER 5 DESCRIPTION OF MODULES 88 ISAC options require multiple thermal bands and are not shown if there are less than 5 thermal bands The surface classes water vegetation soil etc are calculated on the fly employing the surface reflectance spectra from the reflective bands Figure 5 43 shows the panel with the two options for haze over land processing as explained in chapter 10 5 3 Compact Smaller Area Haze Mask Large Area Haze Mask superior in most cases we Correct Thin to Mediun Haze Correct Thin to Moderately Thick Haze superior in most cases Figure 5 43 Options for haze processing The panel of figure 5 44 pops up when the s
38. is the full file name including the path filename should have the last four char acters as _ele and the extension bsq to indicate a digital elevation file and to enable an automatic processing e g example_DEM25m ele bsq pixelsize is specified in meters dem_unit is the integer code for the DEM height unit 0 represents mj 1 means dm 2 means cm The option dem_unit 0 is default and can be omitted The keyword unders specifies the undersampling factor in pixels to reduce the execution time for very large files The default angular azimuth resolution is azi_inc 10 degrees and the default elevation incre ment is ele_inc 30 degrees However the recommended resolution is 10 degrees for azimuth and 5 degrees for elevation In case of large files an undersampling factor gt 1 can be specified to reduce the execution time Usually an undersampling factor of 3 is sufficient A high an gular resolution is more important than a low undersampling factor The output file replaces the ending _ele with the ending _sky e g example_ DEM25m_sky bsq A TIFF input elevation file is converted into the corresponding ENVI elevation file e shadow_batch input filename pixelsize 10 0 solze 30 5 solaz 160 8 dem_unit 0 The keywords have the same meaning as for skyview_batch solze is the solar zenith angle degr and solaz is the solar azimuth angle degr In particula
39. iterations are performed A corresponding notice on the final visibility is given in the atm log output file The set of visibility grid point is given in Table 10 2 The iteration capability is most important for low visibility start values 10 4 2 Aerosol retrieval and visibility map If a sensor has the appropriate spectral bands the aerosol type and visibility or optical thickness of the atmosphere can be derived provided the scene contains reference areas of known reflectance behavior Kaufman and Sendra 1988 Kaufman et al 1997 The minimum requirements are spec tral bands in the red and near IR If the scene contains dense dark vegetation DDV coniferous type the reflectance values in the red band can be obtained from a correlation with the SWIR band reflectance as detailed below The visibility of each reference pixel can then be calculated in the red band as the intersection of the modeled at sensor radiance curve with the measured radiance see figure 10 11 The measured radiance for a reference pixel of digital number DN is L co DN which is a constant value indicated by the dashed line in figure 10 11 The curve indicates the modeled radiance It employs the reflectance of the reference surface e g Pref 0 02 and uses values of path radiance atmospheric transmittance and global flux for the current solar and viewing geometry stored in precalculated LUT Automatic masking of reference areas 1 6
40. p blue p NIR lt p green p SWIR1 lt 0 15 NDSI lt 0 2 10 54 For optically thick clouds it is not possible to distinguish clouds over water from clouds over land if only spectral criteria are used Cloud shadow CHAPTER 10 THEORETICAL BACKGROUND 204 Pixels must satisfy the conditions 0 04 lt NIR lt 0 12 and p SWIR1 lt 0 20 10 55 and they should not belong to the water class This may also include building shadow pixels Snow ice Pixels must satisfy the conditions p blue gt 0 22 and NDSI gt 0 6 and DN blue lt Tsaturation 10 56 The condition DN blue lt Tsaturation means that saturated pixels in the blue spectral band are not included in the snow mask instead they are put into the cloud class If no blue band exists a green band around 550 nm is taken However if the blue or green band is saturated and NDSI gt 0 7 then this pixel is assigned to the snow class because of the very high probability If a green band and a SWIR2 band around 2 2 um exist the following relationships are used DN blue lt Tsaturation and p blue gt 0 22 NDSI gt 0 6 or p green gt 0 22 NDSI gt 0 25 p SWIR2 p green lt 0 5 10 57 Again if the blue or green band is saturated and NDSI gt 0 7 then the snow class is assigned Cirrus over land and water The apparent cirrus reflectance is calculated in the cirrus band 1 38 um Cirrus classes are defined according to the apparent re
41. showed the superior performance of this model if compared to others However for high spatial resolution instruments also other models may be applicable Selected BRDF kernels The BRDF correction scheme is based on the Ross Thick Li Sparse Model RTLS potentially enhanced by the Hot Spot function as proposed by Maignan et al 55 For the correction a formulation of the model for the Bidirectional Reflectance Factor BRF is used The BRF is well suited for correction of the HDRF as both quantities are defined as 1 0 for a 100 reflecting target at the same observation geometry and as only the second dimension observation direction relative variation of the BRF is used for the correction The generic RILS equation of the BRF for each pixel and spectral band is given as PBRF Piso Tool vel r J eal Geos 10 128 where piso is the isotropic reflectance defined at nadir for both illumination and observation angle The kernel factors fy and fgeo are weighting coefficients for the respective kernels They depend on the ground coverage BRDF whereas the kernels are fixed functions which define a fully bi directional reflectance property The kernels have been selected according to the findings of BRDF literature 100 For the volume scattering the Ross Thick kernel is modified to include the hot spot extension by Maignan i e 4 1 TT 1 A oe inc 10 12 37 cos 6 cos 6 3 c cos sinc 3 ne Koal CHAPTER
42. spectral calibration lt e sa coo e ovinos ss 117 CAL REGRESS radiometric calibration with more than one target 117 Convert monochromanic database to new solar reference function 119 Convert atmlib to new solar reference function 120 MTEF and effective GIFOV 121 Toe help MOni Ae eee eRe ee eae mS eS Ow a 122 Water vapor partial pressure a 141 ACEON aras a SOARES 142 Weight factors of hyperspectral bands e 146 Sensor simulation in the solar region 1 e 147 Graphical user interface of program HS2MS 0 148 TOA radiances for three albedos on a n a a e a a 149 Monochromatic atmospheric database a a 151 Solar irradiance database 153 User interface to convert database from one to another solar irradiance 154 GUI panels of the satellite version of program RESLUT 155 Surface temperature error depending on water vapor column emissivity 0 98 173 Surface temperature error depending on water vapor column water surface 174 Spectral emissivity of water Symbols mark the TIRS channel center wavelengths 174 Surface temperature error depending on water vapor column emissivity 0 95 175 Pe OL GP kk eee Ea oO eS He 176 Solar and view geometry ee 177 Main processing steps during atmospheric correc
43. 0 48 0 5 po 66 0 005 10 78 The offset 0 005 for the blue band yields a better correlation with ground meseurements than a zero offset 51 Therefore it is included starting with the 2013 release of ATCOR This situation is sketched in figure 10 12 The correlation factor of 0 5 between the 2 2 wm and the red region is not a universal constant but may typically vary between 0 4 and 0 6 The correlation actually also works for dark soils So the dark pixels may also include soil areas For narrow band hyperspectral sensors a band close to 2 13 um is used instead of a 2 20 um band The red band is then used to calculate the visibility compare figure 10 11 as the intersection of the measured radiance with the simulated visibility dependent at sensor radiance curve Since the same visibility is employed for the blue spectral band this provides an opportunity to adjust the CHAPTER 10 THEORETICAL BACKGROUND 210 L Lptt Puf Ep 7 _ modelling atm LUT sun geometry m E measurement P 190 Visibility km Ct Figure 10 11 Schematic sketch of visibility determination with reference pixel spectral behavior of the path radiance which is essentially the aerosol path radiance since the Rayleigh path radiance is known in the blue spectral region d Le Lbtue TuePiiue abuei T 10 79 Deep blue channels For most multispectral sensors a blue channel in the 470 490 nm region was the shortest wave length
44. 1 bTs z Vsky x y 10 18 The sky view factor can be computed from local information as Vsgy x y cos On z y 2 based on the local DEM slope angle On ATCOR uses the horizon algorithm that provides a more accurate value of the sky view factor by considering the terrain neighborhood of each pixel Dozier et al 1981 Vsky and Vierrain are related by Veky t y 1 VenmaalE y 10 19 CHAPTER 10 THEORETICAL BACKGROUND 192 10 1 2 Illumination based shadow detection and correction For high resolution imagery the correction of cast shadows and illumination on the basis of a surface model does not lead to useful results as the surface representation with respect to the radiometry is never accurate enough Severe over and under correction artifacts are observed due to these inaccuracies in the resulting images The detection and correction of cast shadows has been widely studied specifically for space borne high resolution instruments 3 92 A new method for cast shadow detection has been implemented for the ATCOR case It produces a continuous shadow field and relies on the fact that all areas in cast shadows are illuminated by diffuse irradiance only The diffuse illumination is caused by scattering and thus exhibits very specific spectral characteristics if compared to the direct irradiance Specifically the signal in the blue spectral band is significantly higher in cast shadow areas than in directly illuminated areas
45. 1 where 0 indicates no direct illumination full shadow and 1 means full direct illumination The histogram of is used to rescale the image data Fig 10 23 shows a schematic sketch of such a histogram with a smaller peak at 2 representing the shadow pixels and the main peak at 4 representing the majority of the fully illuminated areas The statistical assumption is used that full direct solar illumination is already obtained for pixels with P x Yy Pmax Then the values are linearly mapped from the unscaled min Piar interval onto the physically scaled 0 1 interval where the scaled shadow function is named P Pinin Oraz e Din 1 if gt mar 10 117 if lt Pma 10 116 The smallest value of the scaled shadow function is 5 0 which means no direct illumination However to avoid overcorrection and to cope with scenes containing merely partial shadow areas it is advisable to set at a small positive value This value of 95 i e the minimum fractional direct illumination deepest shadow in a scene typically ranging between 0 05 and 0 10 is scene dependent see the detailed discussion below histogram 1 0 0 8 a 0 4 0 2 EN 0 0L 1 0 O 5 O G D5 1 0 unsealed shadow function Figure 10 23 Normalized histogram of unscaled shadow function In principle the de shadowing could now be performed with the physically scaled function which represent
46. 5 19 5 20 9 21 ice 5 23 5 24 5 25 5 26 9 21 5 28 5 29 9 30 9 31 9 32 5 99 5 34 9 30 9 36 5 9 9 39 9 39 5 40 9 41 9 42 5 43 5 44 5 45 5 46 5 47 5 48 5 49 5 50 Display of ENVI imagery sacos sora a Ses Simple text editor to edit plain text ASCII files Resize ALCOR input imagery cc eb we eRe REE eww HOD Plotting the explicit sensor response functions 2 a e a e a PROC SCIPIO Te ne ka we He ee Oe Ow Oe ee eG Read sensor meta file ce oc ewe ee Ree eR RHEE He eS Displaying a calibration file same file as in Fig 5 8 Panel to edit the ATCOR preferences ooa a a a a a The Sensor Memi ca ao eoa sa uada art adaa due diaa k a ES Sensor definition files the three files on the left have to be provided created by the E a e dass Definition of a new sensor sooo a a apecical Filler Lrootion izo rra Application of spectral shift to sensor a a a a Black body function calculation panel 1 0 0 ee ee Panels of RESLUT for resampling the atmospheric LUTs Topogtaphic modules 2444504846044 skate oH eade palta A Import DEM from global elevation data SRTM gt oaaae Import DEM from ARC GRID ASCII e DEM PCO esos pakena aso Slope Aspect Calculation panel e e ei ae A MA Example of a DEM left with the corresponding sky view image right Panel of Cast Shadow Mask Calculation SHADOW
47. 5 59 5 60 5 61 9 62 9 63 9 64 5 65 5 66 5 67 5 68 5 69 5 70 5 71 9 12 9 13 9 14 9 10 5 76 5 17 5 78 dl ia 8 1 a2 8 3 8 4 q de 9 3 9 4 9 5 go 9 7 9 8 9 9 9 10 10 1 TU modulo ak we eee ee KHER AA Ow AA eG 93 Filler modules on ee NOE Oe Rew EG RR RE He Swe ee Ew RG 94 BREFCOR correction panel satellite version 2020000202 95 Nadir TOMANDO lt a HS eRe HE we Rew ww 96 MOSCAS OG aoe eG Ae wea oO ee ee oe ee ee ee 98 Filter modil s s s ee oR rras aros ae GS eR 99 Resampling of a reflectance spectrum 000 ee ee ee ee 99 Low pass filtering of a reflectance spectrum 2 000020 es 100 Statistical spectral polishing a aoa o a e a 101 Radiometric spectral polishing ao aooaa a ass 101 Flat field radiometric polishing e o 102 Pushbroom radiometric polishing ao 103 Spectral smile interpolation sa a rs irse 104 Shadow border removal tool a a 106 Simulation modules menu 2 64 6 eee a a 107 Apparent Reflectance Calculation a a a ee 108 The tools Men s o sararae iso roer ARA 109 Calculation of sun angles 1 a 109 Examples of reflectance spectra and associated classes a a 111 SPECL spectral classification of reflectance cube 0008004 111 Example of classification with SPECL 0 0 0 2 00 000084 112 Spectral smile detection 1 a e e a 115 SPEOTRAL CAL
48. 6 Spectral Calibration Atm Absorption Features 116 5 8 7 Calibration Coefficients with Regression 0 0080 eee 117 5 8 8 Convert High Res Database New Solar Irradiance 119 5 8 9 Convert atm for another Irradiance Spectrum 119 5 8 10 MTF PSF and effective GIFOV 0 0 0 00 02 020084 119 Boy Meni HeD sn eo ke eb ee A OE Ae ES Go ee SO 122 Ook Help Options 244 4584 4 RAG asa nap Ree EE EERE eS HE HESS 122 6 Batch Processing Reference 123 Bl Starting ATCOR fom console 6 2 44644 bE Oe dio CO ewe REED Ge HH Es 123 6 2 Using the batch mode from within IDL 020 4 124 6 3 Batch modules keyword driven modules 00000000 125 6 4 Meta File Reader 2 2 4 4 45 Salk ee ORE Re aaa 135 7 Value Added Products 137 ek MAL PP AOS osorno aaa 137 7 2 Surface energy balance e 139 8 Sensor simulation of hyper multispectral imagery 145 9 Implementation Reference and Sensor Specifics 150 9 1 The Monochromatic atmospheric database eee 150 9 1 1 Visible Near Infrared region e 150 so TIET escasa densas ases 151 9 13 Database update with solar irradiance e o 152 9 1 4 Sensor specific atmospheric database ee 0 153 9 1 5 Resample sensor specific atmospheric LU T s with another solar irradiance 153 02 Supported VO Dle pes 6 nw he EOS E KEELE EO RER
49. 7 3 137 CHAPTER 7 VALUE ADDED PRODUCTS 138 Solving for LAI we obtain ag VI a1 Dara Pel 7 4 a2 Sample sets of parameters are ay 0 82 a 0 78 a2 0 6 cotton with varied soil types ay 0 68 a 0 50 a2 0 55 corn and ay 0 72 a 0 61 a2 0 65 soybean with VI SAVI Choudury et al 1994 Note Since it is difficult to take into account the parameters for different fields and different sea sons it is suggested to use a fixed set of these three parameters for multitemporal studies Then the absolute values of LAI may not be correct but the seasonal trend can be captured Plants absorb solar radiation mainly in the 0 4 0 7 wm region also called PAR region photo synthetically active radiation ASRAR 1989 The absorbed photosynthetically active radiation is called APAR and the fraction of absorbed photosynthetically active radiation is abbreviated as FPAR These terms are associated with the green phytomass and crop productivity A three parameter model can be employed to approxiate APAR and FPAR Asrar et al 1984 Asrar 1989 Wiegand et al 1990 1991 FPAR C 1 A exp B LAI 7 5 Typical values are C 1 A 1 B 0 4 Again since it is difficult to account for the crop and seasonal dependence of these parameters a constant set may be used for multitemporal datasets to get the typical FPAR course as a function of time The wavelength integrated surface reflectance in a strict sense the hemispheri
50. A Lp A u i 10 44 E r A y and the unscaled ISAC surface emissivity can be obtained with a 07 O 10 45 where Tref is the brightness temperature image in the reference channel The compensated ON can be converted into the equivalent compensated brightness temperature spectrum where most of the atmospheric absorption features are surface radiance spectrum L removed Both the compensated surface radiance and compensated brightness temperature are spectrally consistent with the data and represent the best estimate for the spectral shape The emissivity spectrum jsqc A may exceed the value 1 in certain channels if the maximum brightness temperature of a pixel does not occur in the selected reference channel However a common reference channel is needed in this method to obtain a consistent pixel independent u spectrum of unscaled path radiance Lp and transmittance ple Split window covariance variance ratio SWCVR The method derives the water vapor map from thermal band imagery 44 52 39 The water vapor content W can be retrieved as a function of the ratio Rj of transmittances 7 Tj in two thermal bands i and j W a b Rii 10 46 CHAPTER 10 THEORETICAL BACKGROUND 201 with N E _ E 2 Dix jo TD Ry LE HA 10 47 NN 2 Tix Sl where N is the number of pixels in a moving window box centered at pixel k T is the average brightness temperature in this box and e is the land surfac
51. A calibration file e g chris m1 cal has to be provided in the new sensor sub directory this file is created automatically if using the function Define Sensor Parameters e The RESLUT resample atmospheric LUTs program has to be run to generate the atmo spheric LU T s for the new sensor employing the monochromatic atmospheric database in atcor atm_database These resampled atm files will automatically be placed in a sub directory of atcor atm_lib with the name of the selected sensor RESLUT will also create the resampled spectrum of the extraterrestrial solar irradiance in the appropriate sensor chris_m1 folder see chapter 9 1 4 e g e0_solar_chris_m1 spc Remember that the sensors might have to be specified as scene dependent if the center wavelengths and or bandwidths change so you might need sensor subdirectories such as chris_m1_11april2003 or chris_m1_scene3_29june2004 The next two tables present examples of a sensor definition file for an instrument without thermal bands and with thermal bands respectively Line 1 is retained to be compatible with the airborne version of ATCOR Line 6 is a required dummy to be compatible with previous versions Any mid IR bands are skipped in the processing the surface temperature band itemp_band is appended after the reflective bands as the last channel of the _atm bsq output file CHAPTER 4 WORKFLOW 41
52. ATCOR Process Tiled from inn o 92 Landsat 8 TIRS Calculate Temperature 92 PRO ge ea eee ee eet ewe ee awe ae eee eee eo ee eG 94 BREFCOR Cotrecti n a ssas icas eRe ewe eb ee oe we eG Ge 94 Nadir normalization Wide FOV Imagery 24 96 We ein ek Oe eG OOS ODE AE 97 A ARE 99 Resample a Spectrum a ooo a a a a 99 Low pass filter a Spectrum ooo a a a 99 Spectral Polishing Statistical Filter a 0 100 Spectral Polishing Radiometric Variation 0000846 101 Flat Field Polishing soc een a ROE SKE Ee ED Oe me He HO 102 Pushbroom Polishing Destriping 2 004 102 Spectral Smile Interpolation ee a 103 Cast Shadow Border Removal lt lt lt 4 i446 6464 6 i8 642 8b eo eS 105 PO I sara rr eee ee ES eee ee E 107 TOA At Sensor Radiance Cube o e 107 TOA At Sensor Thermal Radiance 107 At Sensor Apparent Reflectance e 107 Resample Image Cube aa a a a 108 TOONS ce eae eee eee Re ee eh eee eee Pew eee 109 CONTENTS 5 5 8 1 polar Zenith and Azimuth e e 28 ae ee ba wR eH a Oe Dw OS 109 5 8 2 Classification of Surface Reflectance Signatures 00084 110 5 8 3 SPECL for User Defined Sensors 0 0 2 00 0 02 ee eee 110 5 8 4 Adda Blue Spectral Channel e 111 5 8 5 Spectral Smile Detection ooo cooosers src e oe 113 5 8
53. Ac J T A x j 4 2 The first left hand image pixel is x 0 the last right hand image pixel is x n 1 The coefficients a j have to be stored in an ASCII file corresponding to the band sequence The first column must contain the wavelength or band number followed by the five channel dependent coefficients beginning with ay and ending with ay one line per channel The fixed file name is smile_poly_ord4 dat and it has to be located in the corresponding sensor sub directory In the ideal case these coefficients should be derived from laboratory measurements Since an accurate description is only required for channels in atmospheric absorption regions the 5 coefficients can be set to zero for the remaining regions but they must be provided for each channel If all 5 coefficients are set to zero for a certain channel this channel is processed in the non smile mode which will expedite the processing e Optionally the spectral bandwidth FWHM might also depend on the image column Again a 4th order polynomial is used to describe the bandwidth change depending on column posi tion x and channel j Ai x nm bolj b1 J bali z ba J a bali zt 4 3 FWHM z j FWHM j A x j 4 4 The fixed file name is smile_poly_ord4_fwhm dat and it has to be located in the corresponding sensor sub directory It is an ASCII file with 6 columns per channel the first column is the CHAPTER 4 WORKFLOW 43 acr
54. ENVI BIL Image Imports and transforms an ENVI image in BIL band interleaved by line format to the ATCOR default BSQ band sequential format Erdas Imagine Imports an uncompressed image in ERDAS Imagine format to ENVI format specifically suited for DEM data import Landsat 8 OLI TIRS Imports the standard TIF files from Landsat 8 OLI TIRS data to an ENVI BSQ format containing the correct order of bands wavelength ascending for further processing in ATCOR The panchromatic band is not included in this multispectral set of bands Landsat 8 OLI Imports the standard TIF files from Landsat 8 OLI data to an ENVI BSQ format containing the correct order of bands wavelength ascending for further processing in ATCOR The panchromatic band is not included in this multispectral set of bands Hyperion Raw Image Imports a Hyperion image in ENVI format 242 spectral bands for use with ATCOR The number of bands is reduced to 167 according to the sensor definition provided with the software 5 1 6 Export Transformation and export of standard ENVI format outputs to ENVI BIP Image Transforms and Exports an ENVI BSQ band sequential image to BIP for mat ENVI BIL Image Transforms and Exports an ENVI BSQ band sequential image to BIL for mat Multiband TIFF creates an universal multiband TIFF image from the input ENVI file Use the function Display ENVI file see section 5 1 1 to export JPG PNG or TIFF imagery in RGB or grayscale for
55. Eso oe 10 5 For image data the pixel reflectance p may differ from the background reflectance p In this case the signal at the sensor consists of three components as sketched in Fig 10 4 e component 1 scattered radiance path radiance e component 2 radiation reflected from pixel under consideration e component 3 radiation reflected from the neighborhood and scattered into the viewing di rection adjacency effect Figure 10 4 Schematic sketch of solar radiation components in flat terrain Only component 2 contains information on the surface properties of the pixel the other components have to be removed during the atmospheric correction As detailed in 74 the adjacency radia tion L3 consists of two components atmospheric backscattering and volume scattering which are combined into one component in Fig 10 4 CHAPTER 10 THEORETICAL BACKGROUND 188 The radiometric calibration assigns to each digital number DN the corresponding at sensor radi ance L L k colk EN c1 k DN k 10 6 where k indicates the channel number and co c are the calibration coefficients offset and slope For sensors with adjustable gain settings the equation is L k co k ci k DN k 9 10 7 where g k is the gain setting in channel k The atmospheric correction has to be performed itera tively since the surface reflectance and large scale 0 5 1 km neighborhood background reflectance are not known So three
56. NEM and ISAC but ISAC is currently only supported for flat terrain imagery dem_unit 0 m 1 dm 2 cm DEM height unit line fele DEM elevation file name empty line for a flat terrain calculation line 9 fslp DEM slope file name empty line for a flat terrain calculation line 10 fasp DEM aspect file name empty line for a flat terrain calculation CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 163 line 11 fsky DEM skyview file name empty line for a flat terrain calculation line 12 fshd DEM cast shadow file name empty line for a flat terrain calculation rugged terrain empty if calculated on the fly line 13 atmfile atmospheric LUT file name reflective region e If the automatic aerosol type retrieval is intended for batch jobs the usual aerosol identifier in the file name e g rura has to be replaced with auto Example file name without path is aamsrura atm replace it with aamsauto atm The program then uses all aerosol types for the ms mid latitude summer atmosphere in the aerosol type estimate and selects the one with the closest match compare chapter 10 4 2 In the example of the ms case four aerosol types rural urban maritime desert are checked In case of the tr tropical atmosphere only three aerosol types rural urban maritime will be found in the atmospheric library The automatic aerosol type retrieval require
57. O exp a2 T 1 o R2 ln a LBB 1 Lg 10 36 T 10 37 CHAPTER 10 THEORETICAL BACKGROUND 198 For convenience an offset ag is introduced with default ag 0 The offset term can be used to adjust a temperature bias in a scene Example if scene temperatures are too low by 3K they can be raised by setting ap 3 a2 ey E il Inla1 Lgg 1 10 38 Remark The computer implementation of the channel resampled radiance equations is coded to minimize spectral resampling effects 69 70 Temperature emissivity separation For a sensor with n thermal channels there are n equations of 10 34 with n 1 unknowns namely the n surface emissivities plus a surface temperature So the system of equations 10 34 is always underdetermined Several possibilities exist to address this problem Gillespie et al 1986 1998 129 30 Five options are offered by the satellite version of ATCOR e a constant emissivity default e 0 98 independent of surface cover type 10 12 um region for sensors with a single thermal channel For user defined sensors with multiple thermal bands the parameter 2temp_band described in chapter 4 6 1 defines the channel employed for the surface temperature calculation e fixed emissivity values assigned for 3 classes for the selected surface temperature band pa rameter itemp_band described in chapter 4 6 1 e soil 0 96 e vegetation 0 97 else e 0 98 water and undefined class The as
58. Optical Thickness IA i 50 100 150 200 Visibility km Figure 10 14 Optical thickness as a function of visibility and visibility indez 10 4 3 Water vapor retrieval A water vapor retrieval can be included after the aerosol retrieval because the aerosol retrieval does not use water vapor sensitive spectral bands but the water vapor algorithm employing bands around 820 nm 940 nm or 1130 nm depends on aerosol properties The water vapor retrieval over land is performed with the APDA atmospheric precorrected differential absorption algorithm 87 In its simplest form the technique uses three channels one in the atmospheric water vapor absorption region around 820 nm 940 nm or 1130 nm the measurement channel the others in the neighboring window regions reference channels The depth of the absorption feature is a measure of the water vapor column content see figure 10 15 In case of three bands the standard method calculates the water vapor dependent APDA ratio as La p2 u Lo p u wi Li p1 Lip w3 L3 p3 L3 p where the index 1 and 3 indicates window channels e g in the 850 890 nm region and 1010 1050 nm region respectively Index 2 indicates a channel in the absorption region e g 910 950 nm L and Ly are the total at sensor radiance and path radiance respectively The symbol u indicates the water vapor column The weight factors are determined from Rappa p u 10 90 W1 A3
59. P Acharya P K Robertson D C Chetwynd J H and Adler Golden M MODTRAN cloud and multiple scattering upgrades with application to AVIRIS Remote Sensing of Environment Vol 65 367 375 1998 Berk A Anderson G P Acharya P K and Shettle E P MODTRAN5 2 0 0 User s Man ual Spectral Sciences Inc Burlington MA Air Force Research Laboratory Hanscom MA 2008 Brutsaert W On a derivable formula for long wave radiation from clear skies Water Re sources Research Vol 11 742 744 1975 Buettner K J K and Kern C D The determination of infrared emissivities of terrestrial surfaces Journal of Geophysical Research Vol 70 1329 1337 1965 Carlson T N Capehart W J and Gillies R R A new look at the simplified method for remote sensing of daily evapotranspiration Remote Sensing of Environment Vol 54 161 167 1995 Chander G Markham B L and Helder D L Summary of current radiometric calibration coefficients for Landsat MSS TM ETM and EO 1 ALI sensors Remote Sens Environm Vol 113 893 903 2009 243 References 244 13 Choudhury B J Synergism of multispectral satellite observation for estimating regional land surface evaporation Remote Sensing of Environment Vol 49 264 274 1994 14 Lo Choudhury B J Ahmed N U Idso S B Reginato R J and Daughtry C S T Rela tions between evaporation c
60. Select aerosol types solar region F rural urban J maritime J desert The name h99000 symbolizes a satellite altitude The string ww10 in the filename indicates a water vapor column 1 0 g cm2 or cm w20 indicates a water vapor column of 2 0 g cm2 or cm etc Show Selected Files atm_databaserh93000_ww04_rura bp fatm_database h99000_wv10_rura bp fatm_database h99000_wv20_rura bp atm_databaserh93000_ww29_rura bp A Reflective Region w Thermal Region Selected SENSOR CHRIS_MODES Select ATM files er RUN Aloe Cancel OK Figure 9 4 GUI panels of the satellite version of program RESLUT 9 2 1 Side inputs Before running the main processor certain files have to be provided the DEM derived files are not required for a flat terrain Parameter file extension inn This file is written automatically while using the graphical user interface It may also be written externally for use in batch processing See Section 9 3 for a detailed description Elevation file DEM extension demj_ele bsq Elevation file containing the digital terrain model used for processing Format single band ENVI file in either integer unsigned integer or floating point format Default unit meters Slope file extension dem slp bsq Slope angle for each image pixel Format single band ENVI file byte or 16 bit integer or float data type Unit degrees Aspect file extension dem _asp bsq Aspect angle for ea
61. Sensing Vol 41 1056 1061 2003 64 Richter R Derivation of temperature and emittance from airborne multispectral thermal infrared scanner data Infrared Phys Technol Vol 35 817 826 1994 165 Richter R A spatially adaptive fast atmospheric correction algorithm Int J Remote Sensing Vol 17 1201 1214 1996 66 Richter R Atmospheric correction of satellite data with haze removal including a haze clear transition region Computers amp Geosciences Vol 22 675 681 1996 67 Richter R On the in flight absolute calibration of high spatial resolution spaceborne sensors using small ground targets Int J Remote Sensing Vol 18 2827 2833 1997 168 Richter R Correction of satellite imagery over mountainous terrain Applied Optics Vol 37 4004 4015 1998 69 Le Richter R Bandpass resampling effects on the retrieval of radiance and surface reflectance Applied Optics Vol 39 5001 5005 2000 70 Lo Richter R and Coll C Bandpass resampling effects for the retrieval of surface emissivity Applied Optics Vol 41 3523 3529 2002 71 a Richter R and Schlapfer D Geo atmospheric processing of airborne imaging spectrometry data Part 2 atmospheric topographic correction Int J Remote Sensing Vol 23 2631 2649 2002 172 La Richter R and M ller A De shadowing of satellite airborne imagery
62. TIF files including the folder Example landsat8_envi data1 scene_B1 TIF Then a layer stacked ENVI bsq file for the OLI TIRS bands is created 10 channels wavelength ascending without panchromatic named data1 scene OLI_TIRS bsq e hyperion_tif_enur tifname Here tifname is the complete name of the first band of the Hyperion scene each band is a separate TIF All 242 Hyperion TIF files are layer stacked into one ENVI BSQ file The output file name has the extension bsq This module can also be invoked from the main ATCOR menu with File Import Hyperion Image TIF e reslut_batch sensor xxx aero aero h1s h1s h2s h2s ith ith Here xxx is the sensor name corresponding to the atcor3 sensor xxx folder The key word aero can have the values rura urba mari or dese If not specified aero rura is the default For the processing of thermal band LUTs the keyword ith 1 has to be set CHAPTER 6 BATCH PROCESSING REFERENCE 131 e at_derpolish infile outfile nbin respfile rsp smooth lowpass adj Derivative polishing routine PARAMETERS infile file of reflectances to be filtered outfile name of output file to be created nbin number of adjacent bands to use for filtering nbin 1 KEYWORDS respfile response file used for wavelength reference default ENVI header values lowpass perform lowpass filtering only smooth sm
63. a good capability at resolving high spatial frequencies The slider Line averaging can be adjusted between 3 and 9 to evaluate up to 9 parallel lines centered on the user defined line to average the phase effect Results MTF LSF DN_profile Effective GIFOV for all bands can be saved as ENVI spectral library files slb The Status widget indicates the mode of calculation LSF or ESF CHAPTER 5 DESCRIPTION OF MODULES 121 Quit Select image file Red 230 Green 128 Blue ize Display RGB il a zoom sub scene Line averaging lines to average 25 1111 nm blue 14170m green 379 nm red o IM brown Calculate MTF Save MTF and DN Profiles Status gt LSF evaluated lt lt 9 Wat Spatial Frequenc oe a W Wavalength gn Figure 5 77 MTF and effective GIFOV CHAPTER 5 DESCRIPTION OF MODULES 122 5 9 Menu Help Finally the Help menu allows browsing of the ATCOR user manual provides a link to the web resources contains the software update center and displays license information Satellite ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Licensed for Daniel Version 9 0 0 c DLR ReSe Browse Manual Browse Tutorial Web Resources About Check for Updates Install Components Your License Figure 5 78 The help menu 5 9 1 Help Options The options of the help
64. a reduced set of bands Variable Visibility aerosol optical thickness Yes No Variable Water Vapor 2 osc scices cas os ce ceeaeeseekececwes Yes Q No cage y a ea Deo A AO Yee No FIZ ae E ls E A escena so non O o cocos ococ ono gt Yes No Haza e E GUINE la pesocororooccco ano dooooonon Yes No Shadow Removal Clouds Buildings oooommmoooorrm Yes No Shadow Removal Clouds Buildings oooommmooorroo o o gt Yes No valve ete eimen rod oo ONO Yes No Valus Added Products ooronrrmmmsrcrrcnrrrcnarcarincs Yes No Cirrus Removal aicrrarnciocanacin ranas enero cord caa Yes Q No Sl a Yes No e E al ssonecenosocoonosononoscoooconccne Yes No Cancel OK Cancel 0K Figure 4 7 Image processing options Right panel appears if a cirrus band exists Depending on the selected image processing option some additional panels may pop up Most of them are listed in chapter 5 5 2 but they are self explaining and will not be discussed here They CHAPTER 4 WORKFLOW 39 A e Update DEM Path Path Yexport data data7 atcor2 3 deno_data tn_freib_rusged Mandatory Files Elevation tn_blforest_30n_ele bo3 DEM height z unit 4 m dm cm Slope degree n_blforest_30n_sIp bo3 Aspect degree fn_blforest_30n_asp bsq Optional Files Sky View Factor 2 ln_blForest_30n_sku bog Cast Shadow 0 1 i Use pre calculated shadow file if existing 2 Shadow map calculat
65. across track brightness gradients that appear after atmospheric correction are caused by BRDF effects because the sensor s view angle varies over a large range In extreme cases when scanning in the solar principal plane the brightness is particularly high in the hot spot angular region where retroreflection occurs see Figure 2 8 left image left part The opposite scan angles with respect to the central nadir region show lower brightness values A simple method called nadir normalization or across track illumination correction calculates the brightness as a function of scan angle and multiplies each pixel with the reciprocal function compare Section 10 6 1 Figure 2 8 Nadir normalization of an image with hot spot geometry Left reflectance image without BRDF correction Right after empirical BRDF correction The BRDF effect can be especially strong in rugged terrain with slopes facing the sun and others oriented away from the sun In areas with steep slopes the local solar zenith angle 6 may vary from 0 to 90 representing geometries with maximum solar irradiance to zero direct irradiance i e Shadow The angle P is the angle between the surface normal of a DEM pixel and the solar zenith angle of the scene In mountainous terrain there is no simple method to eliminate BRDF CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 26 effects The usual assumption of an isotropic Lambertian reflectance behavior often causes an
66. additionally For a flat terrain ASCII spectra of the direct diffuse and global flux on the ground are provided in the folder of the input scene see chapter 10 1 4 In case of a flat terrain the global i e direct plus diffuse flux image is calculated For a rugged terrain the images of the direct and diffuse fluxes are calculated Note as the flux files have to use a float encoding 32bits pixel the file size is twice or four times the size of the input scene for a 16bit pixel and 8bit pixel input scene respectively Notice concerning visibility iterations ATCOR will automatically iterate the initial visibility parameter visib set in the inn file if the number of negative reflectance pixels is larger than 1 of the scene for the red band around 650 nm vegetation is checked here or the NIR band around 850 nm water is checked here The specified visibility is always kept if the visibility is set to a negative value i e visib 20 means the program performs the calculation with visib 20 km and does not iterate even if a large number of negative reflectance pixels occurs CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 168 If the parameter npref is set to 1 the program computes the visibility map based on dark reference pixels in the scene and npref 1 overwrites the initial value of the visib parameter With npref 1 the program still iterates the average visibility of the visibility map by checking for water pixels
67. all detector columns For each spectral band the average of all smiled center wavelengths is calculated and used as the new reference wavelength nominal position ENVI header the nominal position as provided in the ENVI header file is taken as the reference Output A cube containing the spectrally interpolated image data is generated and the ENVI header is updated for options 1 and 2 5 6 8 Cast Shadow Border Removal This routine is used after cast shadow correction in case the borders of the shadows show dark or bright artifacts It corrects the imagery by adjusting the brightness to the direct neighbor pixels Using the illumination file all pixels at the cast shadow borders are first identified and a buffer along the borders is calculated using the border width Secondly a brightness correction factor is calculated for each pixel in the border lines using the relative brightness in comparison to its neighbours The pixels are then corrected using this relative difference in a multiplicative way The inputs are as follows see Fig 5 63 Inputs Input File A hyperspectral image cube usually the output of atmospheric correction _atm bsq Mlumination Cast Shadow File illumination file containing a shadow mask which had been applied to the image during atmospheric correction Options Two options for alternate processing are available try those in case the artifacts are not well removed adjust spectral mean An ave
68. anisotropy index Further details about this methods can be found in section 10 6 3 CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION Figure 2 10 Effect of BRDF correction on mosaic RapidEye image DLR Zt Chapter 3 Basic Concepts in the Thermal Region Fig 3 1 left presents an overview of the atmospheric transmittance in the 2 5 14 um region The main absorbers are water vapor and CO which totally absorb in some parts of the spectrum In the thermal region 8 14 um the atmospheric transmittance is mainly influenced by the water vapor column ozone around 9 6 wm and CO at 14 um Fig 3 1 right shows the transmittance for three levels of water vapor columns w 0 4 1 0 2 9 cm representing dry medium and humid conditions The aerosol influence still exists but is strongly reduced compared to the solar spectral region because of the much longer wavelength So an accurate estimate of the water vapor column is required in this part of the spectrum to be able to retrieve the surface properties i e spectral emissivity and surface temperature Transmittance Transmittance 8 10 12 14 8 9 10 11 12 13 14 Wavelength zem Wavelength jm Figure 3 1 Atmospheric transmittance in the thermal region Similar to the solar region there are three radiation components thermal path radiance L1 i e photons emitted by the atmospheric layers emitted surface radiance L2 and reflected radiance D3 In the therm
69. band The time per band increases for channels in atmospheric water vapor regions it decreases in regions where interpolation is applied e g around 1400 nm However the time also depends on other factors such as the overall CPU load in case of multi user machines or the traffic on net worked machines Accordingly the estimate for the remaining time is not always continuously decreasing but may increase sometimes CHAPTER 5 DESCRIPTION OF MODULES Figure 5 46 Value added panel for a flat terrain Figure 5 47 Value added panel for a rugged terrain 90 CHAPTER 5 DESCRIPTION OF MODULES 91 Figure 5 48 LAI FPAR panel Figure 5 49 Job status window CHAPTER 5 DESCRIPTION OF MODULES 92 5 4 12 Start ATCOR Process Tiled from x inn This is a way to start a tiled process of ATCOR from within the ATCOR GUI instead of the standard batch based process atcor_tile The process requires that an x inn file has been created before by going through the atcor GUI or by editing a respective ASCII file manually or by using the routine write_atcor_inn_file pro provided in the directory docu of the ATCOR installation The below parameters are to be entered for processing Input file name name of image data cube to be processed The file must be accompanied by a valid inn file for processing Name of output cube to be created Number of tiles in X and Y dimensions the total number of tiles to pr
70. bit and 16 bit signed or unsigned data no threshold is defined for 32 bit integer or float data As an example for an 8 bit pixel data encoding we obtain T 0 9 255 230 and pixels with a grey value greater than 230 are flagged as truly or potentially saturated Although the saturation nominally starts at D Ninas some sensors already show a non linear behavior around 0 9 x DNmaz so the factor 0 9 is a precaution to be on the safe side This saturation check is performed for two channels in the visible region blue band around 470 500 nm and a green band around 550 nm For multispectral sensors the blue band CHAPTER 4 WORKFLOW 45 geocoded background shadow thin cirrus water medium cirrus water thick cirrus water land saturated snow thin cirrus land medium cirrus land thick cirrus land thin medium haze land medium thick haze land thin medium haze water medium thick haze water cloud land cloud water water 0 1 2 3 4 5 6 7 8 9 10 11 e pp Im oF RP W NO cirrus cloud cirrus cloud thick ER CO 00 Table 4 4 Class label definition of hew file is usually the critical one concerning saturation For hyperspectral sensors with many blue bands the one closest to 450 nm is taken Although the haze cloud water file contains saturated pixels based on two visible bands the percentage of saturated pixels for all bands will be given in the corresponding log
71. channel in the past However with the availability of Worldview 2 and Landsat 8 OLI and hyperspectral data there is often a need to adjust the path radiance in the deep blue region lt 470 nm because the limited set of discrete aerosol models might not be adequate Therefore an empirical approach is taken the DDV surface reflectance at 400 nm is set to 0 6 times the reflectance of the standard blue 480 nm channel and the reflectance for channels in between is linearly interpolated B am 6 a 1 10 80 dd dd PoBo E P0400 A po ar A 0 400 10 81 pe A Po 400 0 480 0 400 Multispectral sensors usually have only one or two channels in the 400 470 nm region In case of hyperspectral instruments the channels closest to 410 nm and 440 nm are selected as tie channels to calculate the corresponding path radiance and exponential interpolation is used to obtain the path radiances for the remaining bands in the 400 480 nm region The question of an automatic aerosol type calculation is addressed next It is restricted to the available number of aerosol models and uses path radiance ratios of the 0 66 0 48 um channels derived from the scene DDV pixels and the corresponding path radiance in the MODTRAN LUTs CHAPTER 10 THEORETICAL BACKGROUND 211 surface reflectance j f ban DO Y as 0 48 0 66 0 80 1 6 22 Figure 10 12 Correlation of reflectance in different spectral regions Aerosol type estimat
72. coefficient of the DDV reference pixels Finally the aerosol optical thickness AOT at 550 nm is calculated with eq 10 89 The visibility index and AOT 550nm maps are stored as separate files The visibility calculation based on the reference pixels has to account for the adjacency effect because reference areas are embedded in non reference areas see the sketch below Since the weighting fraction of reference to non reference area within the adjacency range is not known for each pixel the visibility calculation is performed with an average adjacency weighting factor of 0 5 q acency range al a E A i ras ss T ER UE ET Ohta eps non Tererence Figure 10 31 Weighting of q function for reference pixels Ladjcor Co a DN 0 5q DN DNw clear 10 137 L VIS Lp T Prep Eg Ladj cor 10 138 Next the visibility is converted into the nearest visibility index vi range 0 182 compare Fig 10 14 to store the visibility index map as byte data Spatial interpolation is performed to fill the gaps for non reference pixels or the average vi value can be taken to close the gaps A moving average window of 3 km x 3 km is employed to reduce the influence of noise The cloud building shadow map is stored separately fshd bsq file containing the fraction of direct solar irradiance per pixel scaled with the factor 1000 The scaled value 1000 indicates full solar irradiance smaller values a correspo
73. displayed in Fig 10 8 Advantages of this method are e terrain and forest cast shadows are corrected e water and cast shadows are discerned in most cases e operational usability has been proven on large ADS data sets and e aconsistent physically based method applicable to photogrammetry and imaging spectroscopy is implemented 10 1 3 Integrated Radiometric Correction IRC The IRC method was published by Kobayashi and Sanga Ngoie 45 46 to provide a combined atmospheric and topographic correction The algorithm is briefly outlined here more details can be found in the original papers The first step is the orthorectification of the scene using a digital elevation model DEM Then the slope and aspect maps are calculated The next step is the calculation of the sky view factor see chapter 10 1 1 The original paper uses the simple equation based solely on the slope angle but with ATCOR a more accurate calculation based on a ray tracing can also be used in case of a steep terrain Then the following quantities are computed keeping the original notation of Kobayashi in most cases T 205 gt 10 24 ho 27 oe Here 6 is the solar zenith angle in radian IF s denotes the slope map in radian then the simple version of the skyview is obtained with h 1 s n 10 25 The cosine of the local solar zenith illumination angle P is given in eq 10 17 Then the surface radiance for each channel Ls is calculated by subtr
74. e solar or thermal at sensor radiance surface reflectance or emissivity The processing of hyperspectral thermal data is currently not supported by the satellite version of ATCOR because of the lack of commercial sensors with these channels However this part is already implemented in the airborne ATCOR A detailed description of the keywords of program toarad follows CHAPTER 8 SENSOR SIMULATION OF HYPER MULTISPECTRAL IMAGERY 147 NER noise Radiance Resampling At Sensor Radiance al atmospheric parameters TOARAD hs sensor ms sensor Reflectance Resampling reflectance Figure 8 2 Sensor simulation in the solar region Keywords for the batch program toarad On the IDL command line program names can be written in lower case letters so as an example toarad instead of TOARAD is used synonymously in this context If toarad is submitted as a batch job the following keywords can be specified e toarad input filename pirelsize pixelsize sz solar_zenith atmfile atmfile elev elevation vis visibility adjrange adjrange scalef scalef The input file name must include the path and the keywords in brackets indicate optional pa rameters If a keyword is set it will overwrite the corresponding parameter from the inn file compare chapters 6 3 and 9 4 To perform a TOA at sensor radiance simulation for a given scene the user has to resample files from th
75. equation 10 123 The left part displays the function G for different values of the exponent b For b 1 the decrease with 6 is strong with a constant CHAPTER 10 THEORETICAL BACKGROUND 233 Br 45 degr top te bottom curves exponent p b 1 3 Function E Function amp exponent b top te bottom curves b 1 2 fr 45 degr b 3 4 Ar 339 degr b 1 0 Ar 65 degr 40 SJ od rae ab 90 40 ord 69 ral ab 90 local illumination angle gi degree local illumination angle gi degree Figure 10 27 Geometric functions for empirical BRDF correction Left Functions G eq 10 123 for different values of the exponent b Right Functions G of eq 10 123 for b 1 and different start values of Br The lower cut off value is g 0 2 gradient For smaller values of b the decrease with 6 is moderate initially but the gradient increases with larger i Currently different functions G for soil sand and vegetation can be selected in ATCOR compare the graphical user interface of Figure 5 45 The function G for soil sand is applied with a wavelength independent exponent b After testing a large number of vegetated mountainous scenes two vegetation modes were finally selected because of their good performance 1 b 0 75 for channels with A lt 720 nm and b 0 33 for A gt 720 nm weak correction 2 b 0 75 A lt 720 nm and b 1 A gt 720 nm strong correction In most of the tested cases the first mode was appropri
76. file The third step calculates the surface reflectance in the red band as a fraction a of the NIR band reflectance Pred A Pnir 0 1 x Pnir 10 84 Similar to the empirical SWIR relationships the coefficient a 0 1 is an average empirical value yielding results in close agreement with the SWIR method in many cases However deviations from the nominal value a 0 1 can vary about 30 depending on biome Before the final step of atmospheric correction takes place the visibility of non reference pixels in the scene can be set to CHAPTER 10 THEORETICAL BACKGROUND 213 the average value of the reference pixels or a spatial interpolation can be applied If the percentage of dark reference pixels with eq 10 84 is less than 2 and if a blue spectral band exists then the atmospherically resistent vegetation index ARVI is employed to include somewhat brighter pixels as reference areas The ARVI is defined as ARVI EA 10 85 PNIRT Prb The asterisk in p indicates a TOA reflectance with the Rayleigh contribution already subtracted and Pro Pred 13 Pred Pblue 10 86 If A1 4 are the lower and upper ARVI thresholds 42 0 9 is fixed then additional dark reference pixels are searched which fulfill 41 lt ARVI lt Ag The inital value is 41 0 8 and it is iteratively decreased to A 0 64 until at least 2 of these additional reference pixels are found or the iteration terminates We use the following s
77. file However the check of the blue and green channel normally captures all saturated pixels The thresholds for the cirrus class definition are described in chapter 10 2 Note cloud or building shadow pixels are not included here they are stored separately file image_fshd bsq 4 9 Processing of multiband thermal data Several options have been implemented to process multiband thermal data see chapter 10 1 5 for details Apart from the final products surface temperature and emissivity intermediate products are available such as surface radiance at sensor blackbody temperature and surface blackbody temperature The intermediate products might be useful to trace back spectral or radiometric problems If mage bsq denotes the file name of the input image then the following products are available e image_atm_emi8 bsq 3 or 4 emissivity classes obtained from an on the fly in memory pre classification vegetation soil sand water The pre classification requires daytime data acquisition and spectral bands in the solar region This file has one channel with the emissivity CHAPTER 4 WORKFLOW 46 values for the specified thermal band or in case of ANEM the pixel dependent values assign the maximum emissivity of all available thermal bands image_atm_emiss bsq contains the spectral emissivity map for all thermal channels image_atm_emiss_lp3 bsq is the same emissivity map but filtered with a 3 channel low pass filt
78. flat terrain area where no digital elevation model DEM is needed Then the panel of Fig 4 6 will pop up First the INPUT IMAGE FILE has to be selected ATCOR requires the band sequential format BSQ for the image data with an ENVI header The TIFF format is supported with some restrictions see chapter 9 2 Next the acquisition CHAPTER 4 WORKFLOW 33 date of the image has to be updated with the corresponding button We work from top to bottom to specify the required information The scale factor defines the multiplication factor for surface reflectance range 0 100 in the output file A scale factor of 1 yields the output as float data 4 bytes per pixel However a scale factor of 100 is recommended if the input data is 16 bit 2 bytes per pixel so a surface reflectance value of say 20 56 is coded as 2056 and is stored as a 2 byte integer which means the file size is only half of the float file size If the input data is 8 bit data then a scale factor of 4 is recommended i e a surface reflectance of 20 56 will be coded as 82 If the input file name is 2mage bsq then the default output file name for the atmospherically cor rected image is 2mage_atm bsq The user may modify the output name but it is recommended to keep the _atm bsq qualifier to facilitate the use of subsequent programs Then the sensor view geometry has to be specified as well as the sensor and the calibration file The atmospheric file contains the l
79. from 0 01 for water vapor columns W gt 1 cm to T cir 0 15 for W lt 0 5 cm So with these thresholds tropospheric aerosols might be misclassified as cirrus in situations with dust storms but this is a necessary trade off In any case those cloud areas are excluded from the map of pixels employed for the aerosol retrieval which is the main purpose The cirrus and boundary layer haze removal options are exclusive i e only one of them can be selected per run CHAPTER 10 THEORETICAL BACKGROUND 223 10 5 6 De shadowing with matched filter Remotely sensed optical imagery of the Earth s surface is often contaminated with cloud and cloud shadow areas Surface information under cloud covered regions cannot be retrieved with optical sensors because the signal contains no radiation component being reflected from the ground In shadow areas however the ground reflected solar radiance is always a small non zero signal be cause the total radiation signal at the sensor contains a direct beam and a diffuse reflected skylight component Even if the direct solar beam is completely blocked in shadow regions the reflected diffuse flux will remain see Figure 10 21 Therefore an estimate of the fraction of direct solar irradiance for a fully or partially shadowed pixel can be the basis of a compensation process called de shadowing or shadow removal The method can be applied to shadow areas cast by clouds or buildings This sec
80. from de shadowing So this flag is only used if the de shadowing option is set and if no external water map is supplied Example scene is 2mage1 bsq and a file mage1_water_map bsq or imagel_hcw bsq exist in the same folder then the flag wat_shd is ignored because an external water map always has the first priority ksolflux 0 file with value added channels not calculated ksolflux 1 value added channels are calculated flx file ishadow 0 and fshd empty string no DEM cast shadow map is used ishadow 0 and fshd valid file name pre calculated DEM cast shadow file is used ishadow 1 DEM cast shadow mask is calculated on the fly The pre calculated map avoids repeated on the fly calculations icl shadow 0 no cloud building shadow correction icl shadow gt 0 cloud building shadow correction is performed 1 de shadowed output as DN image corresponding to input DN scene 2 de shadowed output as surface reflectance image 3 de shadowed output as surface reflectance and DN image If a float _ilu bsq file exists in the scene folder then the de shadowing is performed with this file i e the matched filter algorithm is not applied Otherwise this _ilu bsq has to be renamed temporarily if the matched filter method shall be executed line 21 CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 165 0 0 5 0 5 itriang ratio_red_swir ratio_blu_red itriang
81. from surrounding mountains onto the considered pixel see chapter 10 1 1 This program is also available in the batch mode see chapter 6 3 Input parameters besides the DEM file are e DEM horizontal resolution in meters the x and y resolution must be the same e DEM height unit supported units are m dm and cm e Angular resolution degrees in azimuth and elevation e The undersampling factor of the DEM in pixels For large DEM s the skyview processing may be very time consuming unless an undersampling is chosen here CHAPTER 5 DESCRIPTION OF MODULES 71 DEM File may have 16 or 42 bit integer or float data Input DEM FILE Yexport data data atcor3 demo_data tn_ranon tn_sept35_ele bsq QUIT SkY VIEW FACTOR Vexport data data atcor3 deno _data tn_ranon tn_cept35_sky bog OVERURITE DEM resolution x y pixel size meters 30 6 Undersampling factor for faster processing Pixels E Azimuth resolution degr ao Elevation resolution degr E A high azimuth elevation resolution is very time consuming Undersampling factor of 3 pixels is recommended for large scenes Hessaces Lone 22 sec Figure 5 24 Panel of SKYVIEW Figure 5 24 shows the GUI panel and figure 5 25 presents a skyview image derived from a DEM image An angular azimuth elevation resolution of 10 degrees 5 degrees is recommended For large images it causes a high execution time which can be reduced by selecting an unde
82. geometry as viewed from the scene center is specified with satEl or meanSatEl satellite elevation angle CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 179 and satAz or meanSatAz absolute azimuth angle ATCOR s tilt angle can be calculated from equation 9 9 with the Quickbird orbit altitude 450 km The tilt angle is close to the incidence 90 satEl value see Table 9 2 Depending on the processing date the tilt angle may also be included in the IMD file then it is named offNadirViewAngle or meanOffNadirViewAngle elevation degree incidence degree tilt degree Table 9 2 Elevation and tilt angles for Quickbird The Quickbird sensor uses the radiance unit Wm sr in band radiance which can be con verted into a spectral band average radiance employing the effective bandwidth of each channel specified as AA 0 068 0 099 0 071 and 0 114 um for the blue green red and NIR band respectively reference 49 from http www digitalglobe com The calibration is different for compressed 8 bit data and the original 11 bit data ATCOR contains a template for an 8 bit cal file quickb_8bit_std cal and an 11 bit file quickb_16bit_std cal However it is recommended to use only the 11 bit data for ATCOR The IMD metadata file contains the absolute calibra tion factor absCalFactor for each multispectral channel in the unit Wm sr
83. good results on sample scenes with uniform cover types presented in the above papers When applying the methods to a wider range of areas some of the practical problems are CHAPTER 10 THEORETICAL BACKGROUND 232 e mountainous scenes often contain a number of different covers e g deciduous forest conif erous forest mixed forest shrubs meadow rocks etc e the computation of the C coefficients for different surface covers would require a pre classi fication e the correlation obtained for the C coefficients is often less than 0 7 yielding unreliable results with this method These remarks are supported by reference 63 These authors applied different correction ap proaches to a TM scene containing different cover types and noted that there is no optimum method for all cover types A drawback of the Minnaert and empirical C methods is that they do do not distinguish between the direct and diffuse solar illumination as opposed to the physically based approach of ATCOR Nevertheless the latter approach also cannot avoid problems in faintly illuminated areas Correction method The methods described in the above section are supplemented by an empirical method with three adjustable parameters G7 b and g as explained below This approach was tested on different rugged terrain scenes with vegetated and arid landscapes and usually yields satisfactory results It reduces overcorrected reflectance values starting at a threshold l
84. in Sentinel 2 type of imagery Int J Remote Sensing Vol 32 2931 2941 2011 81 Richter R Heege T Kiselev V and Schl pfer D Correction of ozone influence on TOA radiance Int J Remote Sensing Vol 35 8044 8056 2014 82 Rodger A and Lynch M J Determining atmospheric column water vapour in the 0 4 2 5 um spectral region Proceedings of the AVIRIS Workshop 2001 Pasadena CA 2001 83 Salisbury J W and D Aria D M Emissivity of terrestrial materials in the 8 14 wm atmo spheric window Remote Sensing of Environment Vol 42 83 106 1992 84 Lal Sandmeier S T and Deering D W Structure analysis and classification of boreal forests using airborne hyperspectral BRDF data from ASAS Remote Sensing of Environment vol 69 no 3 pp 281295 1999 85 Lal Santer R et al SPOT Calibration at the La Crau Test Site France Remote Sensing of Environment Vol 41 227 237 1992 00 Santer R et al A surface reflectance model for aerosol remote sensing over land Int J Remote Sensing Vol 28 737 760 2007 87 Schlapfer D Borel C C Keller J and Itten K I Atmospheric precorrected differential absorption technique to retrieve columnar water vapor Remote Sensing of Environment Vol 65 353 366 1998 Lo 88 Schlapfer D and Richter R Geo atmospheric processing of airborne imaging
85. in 2010 The database is called monochromatic because of its high spectral resolution compare figure 9 1 After resampling with the spectral response functions of any sensor a typical size of the sensor specific database is 10 MB Chapter 9 1 4 con tains a description of the resampling program RESLUT 9 1 1 Visible Near Infrared region In the solar spectral region 0 34 2 54 ym MODTRAN was run with different wavenumber spacings to achieve a wavelength grid spacing of approximately 0 4 nm except for the 1400 nm and 1800 nm regions This required the use of MODTRAN s p1_2008 database i e 0 1 cm in the 2 1 2 5 um region In addition different RT algorithms were used in atmospheric window regions the scaled DISORT algorithm with 8 streams SD 8 was employed in absorption regions the more accurate SD 8 with the correlated k algorithm was selected 33 Since the wavenumber erid is not equidistant in wavelength the LUT s were resampled with an equidistant 0 4 nm grid of Gaussian filter functions of FWHM 0 4 nm to speed up subsequent calculations So the new LUT database should be sufficient for instruments with bandwidths gt 2 nm covering the solar spectral region from 340 to 2540 nm The standard database is calculated for nadir viewing instruments An off nadir database with 150 CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 151 the tilt angles 0 30 in steps of 10 is available upon reques
86. in the 1600 nm region if band exists Pixels belong to the water mask if p NIR lt py and py600 lt Pwo The defaults pw1 5 and py 3 allow some margin for turbid water line 5 interpolate bands in 760 nm oxygen region O no 1 yes line 6 interpolate bands in 725 825 nm water region 0 no 1 yes line 7 interpolate bands in 940 1130 nm water region 0 no 1 nonlinear 2 linear line 8 smooth water vapor map box 50m 50m O no 1 yes The water vapor map is calculated on a pixel by pixel basis a moderate spatial smoothing 50m 50m or at least 3 3 pixels reduces the noisy appearance line 9 interpolate bands in 1400 1900 nm nm water region 0 no 1 yes line 10 cut off limit for max surface reflectance default 150 line 11 _out_hcw bsq file haze cloud water land O no 1 yes 2 hcw quality file line 12 water vapor threshold to switch off the cirrus algorithm unit cm line 13 define saturation with factor b DN saturated gt b DN maz b 0 9 to 1 line 14 include non linear influence of vegetation in water vapor calculation yes no Only for water vapor retrieval with regressein iwv_model 2 line 15 start stop wavelengths for interpolation in the 940 nm region line 16 start stop wavelengths for interpolation in the 1130 nm region line 17 start stop wavelengths for interpolation in the 1400 nm region line 18 start stop wavelengths for interpolation in the 1900 nm region line 19 haze sun glint over water apparent NIR
87. in the NIR band unless the specified visib is negative A constant scene visibility is employed for npref 0 In case of scene tiling and npref 0 or npref 1 the iterated visibility obtained for sub scene 1 is also applied to all other sub scenes to avoid brightness steps for the merged sub scenes caused by potentially different visibilities Attention If scene tiling has to be performed and the aerosol map is requested for each sub image then specify npref 1 but this could cause different average visibilities in the sub scenes and potentially brightness steps at the sub scene borders 9 5 Problems and Hints Some often encountered problems and tips to come around are listed here e Distinction of haze and cloud when can the haze removal algorithm be applied Ground surface information under haze areas can still be recognized in the 600 900 nm region but the brightness contrast is low The haze removal is only applied for channels in the 400 800 nm region However for cloud areas no ground information can be observed If in doubt whether a certain area should be assessed as haze or cloud covered take a look at the scene in the NIR around 850 nm channel if surface features can be seen in this area the haze algorithm might be applied If not the area is cloud covered and a haze removal run will not be successful e The cloud mask is not appropiate The cloud mask might contain too few pixels then decrease the cloud reflectance thr
88. m CHAPTER 5 DESCRIPTION OF MODULES 30 5 46 SPECTRA module The SPECTRA module see figure 5 35 serves to extract spectra of different targets of the scene as a function of the visibility It is started from within one of the four possible ATCOR main panels These spectra can be compared to field spectra or library spectra to estimate the visibility Scene derived spectra also may indicate calibration errors in certain channels In that case a copy of the sensor calibration file can be edited to match the retrieved reflectance with the field library spectrum or the inflight calibration module may be employed see chapter 5 4 9 In most cases it is useful to check some scene derived target spectra e g water vegetation or soils before starting the processing of the image cube at the risk of blind processing and obtaining wrong results Select display bands file elect display bands file Select calibration f ile Temperature offset Save last spectrum Red la Green lz Blue la Cal file ln_standard cal Atmosphere Farura Display image z Target box pixels EB Adj range km 1 00 reference spectrum Message 1 fi E e E E g A Ms Visibility km jag Direct plot to Screen 1 w Screen 2 BN DA Wa y E O a AR y E E y A R 4 Lf ae q A Fed Extract Spectrum from x 368 y 437 Calculate yes Ps PR fe lt 7 PA Me L ay Pa 4 E d reflectance 5 EDs De
89. menu are listed below Browse Manual Opens this manual in the default PDF display application of your machine Browse Tutorial Opens the ATCOR 3 tutorial in the default PDF display application of your machine The tutorial can be found in the docu directory of your ATCOR installation Web Resources Opens the html document atcor3_webresources htm in the systems default ap plications for viewing H TML documents About Provides basic information about the copyright and also displays the build number of the software please provide the build number for debugging purposes in case the ATCOR support is contacted Check for Updates Connects to the ReSe Web server to check if a new build release of the software is available The software update is downloaded automatically if available and may be installed thereafter Install Components This is your software update center atmospheric database files may be downloaded and updated directly through this update tool upon availability Your License Provides information about the licensed features in your license key and also dis plays which license key is currently active Chapter 6 Batch Processing Reference For most ATCOR modules a convenient graphical user interface is available but batch jobs can also be submitted A detailed discussion of the interactive panel driven modules is given in chapter 5 Running ATCOR in batch mode can be done in two ways either from the operating syst
90. n of across track image pixels per line If geocoded the scan angle for each pixel must be provided in a separate file _sca It contains the scan angle in degree scaled with a factor of 100 and coded with 16 bits per pixel This definition is taken from the airborne ATCOR PARGE interface Schlapfer and Richter 2002 Scan angles on the right hand side with respect to flight heading are defined as negative those on the left side as positive e g a value of 2930 represents a scan angle of 29 3 on the right side The nadir region is defined here as the 3 scan angle range Usually a 3 angular sampling interval from 3 to FOV 2 on the left side and 3 to FOV 2 on the right side is adequate except for geometries close to the hot spot geometry In the latter case a 1 sampling interval can be selected If bnadir denotes the averaged brightness value for the nadir region i e reflectance or radiance then the nadir normalized brightness value of a pixel with column number j is calculated as Dandie bnorm j DEG 10 122 where the function f2 is obtained with three processing steps e The first step is the averaging over each interval 3 or 1 It yields a function f with m 1 grid points for the m off nadir intervals plus the nadir interval e Two cases are distinguished now if the image is not geocoded an interpolation from function fi m 1 to a function f2 ncols is performed where ncols is the number o
91. new reference function e0_solarx dat N CONVERT_DB3 Convert bp7 Files in atm_database for Another Solar Irradiance File Standard high resolution atmospheric database 340 2547 nm CHRIS Proba database smaller spectral coverage 380 1080 nm High resolution database 1 sre_idl atcor atcor_23 atm_database Solar irradiance file fi Ysro_idl atcor atcor_23 atn_database e0_solar_fonten2011_04nn dat src_id atcor atcor_23 sun_irradiance e0_solar_kurucz1997_04nm dat Solar irradiance file f2 High resolution database 2 Ysre_idl atcor atcor_23 atn_database_kurucz1997 Convert Database 1 irradiance f1 into Database 2 irradiance f2 L AA a QUIT Figure 5 75 Convert monochromanic database to new solar reference function 5 8 9 Convert atm for another Irradiance Spectrum The conversion as described in module 5 8 8 can be applied to a sensor specific atmospheric library of a self defined sensor using this function In the panel as of Fig 5 76 the sensor has first to be entered and the new solar function e0_solar dat is to be selected before the conversion may be applied 5 8 10 MTF PSF and effective GIFOV A useful representation of the spatial resolution of a sensor is the transfer function describable in terms of a modulation transfer function MTF and a phase transfer function The transfer function describes how the system modifies the amplitude M
92. not properly work as the path radiance in the visible especially in the blue becomes small and the following criterion based on surface reflectance instead of apparent reflectance is used p NIR Tuote NIR and p SWIRI lt rater Sw Tt 10 49 where Twater NIR 18 the water reflectance threshold for the NIR band around 850 nm Equation 10 49 is also applied if any threshold Twater NIR Or Twater swIR1 18 set to a negative value In this case the elevation criterion pixel below 1 2 km is overruled Saturated pixels CHAPTER 10 THEORETICAL BACKGROUND 203 These pixels fulfill the criterion DN blue gt DD eee gs 10 50 where DN blue is the digital number in a blue band around 470 nm and the threshold Tsaturation is defined in the preference parameter file If a blue band does not exist a green band around 550 m is used as a substitute If a green band also does not exist a red band around 650 nm is used T saturation b encoding default b 1 0 e g 1 0 255 255 for 8 bit sensors with encoding 255 A lower value e g b 0 9 might be used because nonlinear effects might already occur ot lower radiance levels This would provide some safety margin and indicate situations near saturation Setting b 0 9 for an 8 bit sensor means that pixels exceeding DN 0 9 255 230 will be marked as nearly saturated For a 32 bit encoding integer or float no saturation threshold is defined As saturation usually occurs in the bl
93. number of bands to process Button Select Bands lets you select the bands to mosaic the number of currently selected bands is displayed beneath the button Output File Name name of output file to be written Actions Preview the first of the selected bands is mosaicked at a resolution reduced by a factor of 2 and the result is displayed Run Process The mosaicking is performed Restrictions This routines requires georeferenced data with same coordinate system no rotation in ENVI header background coded with 0 all files should have the same number of bands for mosaicing The input resolutions of the imagery may vary CHAPTER 5 DESCRIPTION OF MODULES Figure 5 55 Mosaicking Tool 98 CHAPTER 5 DESCRIPTION OF MODULES 99 5 6 Menu Filter The Filter menu provides spectral filtering of single spectra reflectance emissivity radiance provided as ASCII files spectral filtering of image cubes and spectral polishing X Satellite ATCOR File Sensor Topographic ATCOR ERDF Filter Simulation Tools A Resample a Spectrum Low pass filter a spectrum Spectral Polishing Statistical Filter Spectral Polishing Radiometric Variation Flat Field Polishing Pushbroom Polishing Destriping Spectral Smile Interpolation Image Cube Cast Shadow Border Removal Figure 5 56 Filter modules 5 6 1 Resample a Spectrum This program serves for the general purpose of resampling It requires an ASCII fil
94. optionally skyview and cast shadow are used to calculate the solar illumination map cosine of the local solar zenith angle which is stored as byte data range 0 100 scale factor 100 If the file name of the input scene is scene bsq then the corresponding illumination file is named scene_ilu bsq However there is an option to process an external illumination map and skip the internal calcula tion In this case the requested condition is a float coded file range 0 1 with the nomenclature scene_raw_tlu bsq first priority or scene_ilu bsq second priority to distinguish it from an internal calculation if an internal calculation was performed previously In case of a rugged terrain the illumination based de shadowing algorithm stores results in a multi layer file and the first layer is the float coded illumination Two de shadowing methods are implemented in ATCOR the matched filter approach see chapter 10 5 6 and the illumination based approach see chapter 10 1 2 The latter delivers a float coded illumination file range 0 1 containing the fractional shadow If a float coded illumination file exists in the folder of the input scene then it is used for de shadowing also for the flat terrain case without DEM files Therefore it has always the first priority If the matched filter de shadowing is intended then the float coded illumination file has to be renamed or temporarily removed from this folder Internally t
95. or 2 2 ym band required or at least red NIR bands CHAPTER 10 THEORETICAL BACKGROUND 209 visibility km vis increment km SS 3 3 3 3 3 3 4 5 5 max VIS 120 km Table 10 2 Visibility iterations on negative reflectance pixels red NIR bands If the sensor has a SWIR band at 1 6 or 2 2 um then the scene can be searched for dark pixels in this band and a correlation of the SWIR reflectance with the reflectance in the red and blue band can be employed to estimate the visibility automatically Kaufman et al 1997 For this purpose we use a modified version of the original idea for the following algorithm If a SWIR band exists the SWIR reflectance is calculated assuming a visibility of 23 km instead of the original version of top of atmosphere reflectance Then water pixels are excluded by employing only those pixels with SWIR reflectance values above 1 and an NDVI gt 0 1 For the 2 2 wm band the upper threshold of the reflectance of the dark pixels is selected as 5 If the number of reference pixels is less then 1 of the image pixels then the upper threshold is increased to 10 or finally 12 If a 1 6 um band exists but no 2 2 um band the corresponding upper thresholds are selected as 10 and 15 or finally 18 respectively The reflectance ratios for the red near 0 66 u and blue near 0 48 wm band are then calculated as Pred 0 5 p2 2 and 0 48 0 5 po 66 0 005 10 77 Pred 0 29 P16 and
96. original RGB image right corrected image data source Leica ADS central Switzerland 2008 courtesy of swisstopo After cast shadow correction the border pixels of the shadows are often under overcorrected which is visible as black or bright borders in the cast shadow areas A filter has to be applied to remove this artifact compare Fig 10 9 A simple approach to this problem is an interpolation of all border pixels However a considerable data loss may be the result of such a process Thus a more sophisticated approach is required As the border pixels are usually isolated a filter approach has been used which compares the pixel brightness p to the brightness of its direct neighbors Pprox in a 3x3 or 5x5 box respectively The brightness of the shadow border pixel is then adjusted by the relative brightness difference of the whole spectrum such that D Pfiltij Pij 10 23 Pij This method proved to successfully remove shadow borders for high resolution imagery and an urban environment However for terrain shadows on a larger scale the border pixels are not such clearly isolated and often can not be corrected using this method or only after increasing the size of the border pixel filter significantly CHAPTER 10 THEORETICAL BACKGROUND 194 Figure 10 9 Effect of cast shadow correction middle and shadow border removal right for building shadows The updated processing leads to improved terrain correction as
97. reader multiplies the B and G values with the factor 0 1 for the unit conversion in the cal file i e co 0 1 B and c 0 1 G For the ETM thermal band two files are included per scene e g xxx nn61 tif and xxx nn62 tif the 61 indicates the low gain the 62 indicates the high gain data To be compatible with Landsat 5 TM only one thermal band should be layer stacked for ATCOR and placed at the band 6 position The meta file reader generates the cal file for the 61 case but the offset gain values co c1 for the 62 case are also given in the panel Either one can be selected for ATCOR but an update editing of the radiometric calibration cal file is nessary for 62 The module Read Sensor Meta File ATCOR main panel entry below File reads the USGS LPGS meta file and extracts the necessary data for the cal and inn files The panchromatic band 8 is not included in the multispectral band list again to be compatible with Landsat 5 TM CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 171 This band has to be treated separately Notice the standard negative offset values can lead to negative surface reflectances for dark targets therefore in these cases the magnitude of the negative offset has to be decreased typically by a factor 2 ETM bands 1 4 Note concerning Landsat 4 5 TM and non LPGS non NLAPS processing Differe
98. reflectance thresholds in the NIR chan nel for clear water and haze are Ti clear 0 04 or 4 and To haze 0 12 or 12 respec tively Pixels with values less than T clear are defined as clear water pixels with values between the thresholds T clear and To haze are assigned as haze or sun glint A lower value i e T clear lt 0 04 can be can be specified but might cause a wrong classification of bright coastal water sandy bottoms and bleached coral waters If the threshold T clear is too high haze pixels might erroneously be put into the clear water category Cast shadow areas mountainous terrain these may contain over and or undercorrected pixels during the standard empirical BRDF correction A reduction of these pixels is tried with the following steps e bright pixels in the NIR with DN gt DN mean o o standard deviation are removed from the list of shadow pixels e a 7 x 7 pixel convolution filter is being applied to the shadow mask e a transition region shadow sunlit is introduced e already dark pixels DN lt DN mean o in the NIR are not reduced in brightness during BRDF correction The threshold is evaluated for a NIR channel but the non reduction of brightness reflectance is applied to all channels 9 4 Job control parameters of the inn file If the file name of the input image is example_image bsq then a file example_image inn is created during the interact
99. seen that are hidden in the uncorrected scene see the zoom images of figure 2 7 The central zoom image represents the shadow map scaled between O and 1000 The darker the area the lower the fractional direct solar illumination i e the higher the amount of shadow Some artifacts can also be observed in Figure 2 6 e g the Isar river at the bottom right escaped the water mask entered the shadow mask and is therefore overcorrected The proposed de shadowing technique works for multispectral and hyperspectral imagery over land CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 24 Figure 2 6 De shadowing of an Ikonos image of Munich European Space Imaging GmbH 2003 Color coding RGB bands 4 3 2 800 660 550 nm Left original right de shadowed image acquired by satellite airborne sensors The method requires a channel in the visible and at least one spectral band in the near infrared 0 8 1 um region but performs much better if bands in the short wave infrared region around 1 6 and 2 2 um are available as well A fully automatic shadow removal algorithm has been implemented However the method involves some scene dependent thresholds that might be optimized during an interactive session In addition if shadow areas are concentrated in a certain part of the scene say in the lower right quarter the performance of the algorithm improves by working on the subset only The de shadowing method employs masks for cloud a
100. shadow pixels and a local minimum at 1 The secondary peak can be determined by level slicing the normalized histogram We arbitrarily define a threshold r as the intersection of this slice line at the level of h 2 with the normalized histogram h for dy lt P lt Pmax The approach with a main peak and a smaller secondary peak is restricted to cases where the percentage of shadow pixels in the scene is less than about 25 This applies to the fully automatic processing mode If the secondary peak at Pa is not clearly defined numerically i e no local minimum found at or histogram difference h 2 h P lt 0 03 then Pr is defined as the intersection of the slice level 0 10 with h for Y lt Pmaz More flexibility exists in the interactive mode see chapter 2 4 figure 5 39 Masking of the core shadow areas with lt r Fig 10 23 is critical like any thresholding process a large threshold could potentially include non shadow areas a low threshold could miss shadow areas The current automatic algorithm has the three user selectable options of a small medium or large core shadow mask corresponding to thresholds set at Py 0 1 Pr and Py 0 1 respec tively The default value for the fully automatic algorithm is the medium size mask In addition an interactive mode for adjusting the threshold is also available A second tunable parameter is the minimum fractional direct illumination 5 also called depth
101. side of equation 7 7 are called heat fluxes The soil or ground heat flux G typically ranges from 10 to 50 of net radiation Convection to the atmosphere is called sensible heat flux H It may warm or cool the surface depending on whether the air is warmer or cooler than the surface The energy available to evaporate water from the surface LE is usually obtained as the residual to balance the net radiation with the dissipation terms Net radiation is expressed as the sum of three radiation components En Esolar T Ratm sur face 7 8 where Rsolar is the absorbed shortwave solar radiation 0 3 3 um or 0 3 2 5 um Ratm is the longwave radiation 3 14 um emitted from the atmosphere toward the surface and Reur face is the longwave radiation emitted from the surface into the atmosphere Downwelling radiation is CHAPTER 7 VALUE ADDED PRODUCTS 140 counted with a positive sign the upwelling thermal surface radiation has a negative sign The absorbed solar radiation can be calculated as 2 5m Rear f LANE dA 7 9 0 3um where p A is the ground reflectance 1 p A is the absorbed fraction of radiation and E A is the global radiation direct and diffuse solar flux on the ground The numerical calculation of equation 7 9 is based on the same assumptions regarding the extrapolation of bands and interpolation of gap regions as discussed in chapter 7 1 dealing with the surface albedo If the satellite imagery contai
102. surface is often contaminated with cloud and cloud shadow areas Surface information under cloud covered regions cannot be retrieved with optical sensors because the signal contains no radiation component being reflected from the ground In shadow areas however the ground reflected solar radiance is always a small non zero signal be cause the total radiation signal at the sensor contains a direct beam and a diffuse reflected CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 23 L radia nee AS v targets least squares fit o 3009 1000 1500 2000 ON digital number Figure 2 4 Radiometric calibration with multiple targets using linear regression skylight component Even if the direct solar beam is completely blocked in shadow regions the reflected diffuse flux will remain see Fig 2 5 Therefore an estimate of the fraction of direct solar irradiance for a fully or partially shadowed pixel can be the basis of a compensation process called de shadowing or shadow removal The method can be applied to shadow areas cast by clouds or buildings Figure 2 5 Sketch of a cloud shadow geometry Figure 2 6 shows an example of removing building shadows The scene covers part of the central area of Munich It was recorded by the Ikonos 2 sensor 17 Sept 2003 The solar zenith and azimuth angles are 46 3 and 167 3 respectively After shadow removal the scene displays a much lower contrast of course but many details can be
103. the dehazing and the interpolation method is also included Additionally a file _haze_map bsq is created containing the classes haze land haze water clear water and geocoded background IF the keyword water 1 is specified then the corresponding names are dhw dh1w etc Typing the name of the module without parameters will yield the list and description of the parameters This program can also be invoked from the ATCOR main panel CHAPTER 6 BATCH PROCESSING REFERENCE 130 e specl2_batch input filename sensor xx or specl2_tile input filename sensor xx ntr ntx nty nty The spectral classification based on template reflectance spectra is also available in the batch mode and with the tiling option The xx is a keyword for the sensor type e g xx landsat 7 for Landsat 7 ETM The complete list of sensor keywords is shown when typing specl2_batch on the IDL command line without the sensor specification The ntx nty keywords have the meaning explained for the ATCOR tile programs above e smile_interp3_batch input filename fpoly fpname option number silent silent Purpose The atmospheric correction accounts for the column dependent smile shift as spec ified in the smile_poly_ord4 dat of the corresponding sensor folder but the image columns of each band belong to slightly different wavelengths This function interpolates the pixel reflectance values for each band to a specified reference wavelen
104. the scale factor s is accompanied with a truncation of surface reflectance values at O in the output cube So a negative reflectance e g caused by a wrong choice of vis ibility or inaccurate radiometric calibration will be reset to zero in the output image In the SPECTRA module no truncation is applied If a user wants the output reflectance cube without zero truncation the scale factor s should be specified with a negative value e g s 1 will provide a float output surface reflectance retaining negative reflectance values s 100 will provide a 16 bit integer output file The byte scale 10 lt s lt 1 and output data range 0 255 cannot be used to represent negative values The negative scale factor should only be used for test purposes since the results do not make a physical sense and some further processing options or modules are excluded in this case e g the value added calculation of surface energy balance components the automatic CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 157 spectral classification SPECL or the BRDF corrections Note The TIFF format is also allowed for the input file However some restrictions apply in this case compare chapter 6 3 e All bands in one TIFF file the channels in the file must have the ascending band order and the maximum number of channels is 9 e Each band in a separate file file names must include the band numbers e g image_band1 tif image_ba
105. these high overcorrected values depending on the illumination and or viewing angles The third method corrects the observation BRDF effects by fitting a physical BRDF model to a number of images and surface cover classes in order to obtain a generic BRDF correction function This function is used to calculate a per pixel anisotropy factor which corrects for the deviation from an averaged spectral albedo In some cases of rugged terrain imagery it is useful to apply both incidence and observation angle correction methods of BRDF correction 10 6 1 Nadir normalization method A simple algorithm was implemented as part of the ATCOR package to normalize the scan angle dependent brightness values to the nadir value It is recommended to apply the method to imagery CHAPTER 10 THEORETICAL BACKGROUND 230 after atmospheric correction i e to reflectance data However if only the across track illumination gradients shall be removed without any further atmospheric correction the algorithm can also be applied to radiance DN data In this case the brightness gradient may be caused by a combination of surface BRDF and atmospheric BRDF left right asymmetry in path radiance The algorithm is intended for large field of view sensors minimum FOV 20 It computes the column means with a certain angular sampling interval 1 or 3 The input image may be geocoded or not If it is not geocoded the total field of view FOV corresponds to the number
106. they are based on the e _solar_kurucz1997_06nm dat irradiance Beginning with the 2011 release the directory of the atm files contains an ASCII file named irradiance_source txt identyfying the underlying solar irradiance file 9 1 4 Sensor specific atmospheric database This sensor specific database is created by resampling the files of the monochromatic database with the sensor s spectral response functions employing program RESLUT see figure 9 4 An aerosol subset or all aerosol files from the monochromatic database can be selected All water vapor files belonging to the selected aerosol type e g wv04 wv10 wv20 wv29 wv40 will be resampled for hyperspectral or user defined sensors The folder with the atm files also contains a file ir rad_source txt identyfying the underlying solar irradiance spectrum In addition a file of the resampled extraterrestrial solar irradiance e g e0_solar_chris_m1 spc will be created in the corresponding sensor folder e g sensor chris_m1 9 1 5 Resample sensor specific atmospheric LUTs with another solar irradiance It is also possible to resample existing sensor specific LUTs atm files with another solar irra diance spectrum Input is a sensor from the atcor sensor folder example name xxx with the corresponding spectral response files rsp and a high resolution solar irradiance file from the atcor sun ir
107. to the sensor as described in section 5 2 3 Input to the spectral calibration module are the DN spectra of selected fields saved as ASCII files in the SPECTRA module by pressing the button Save last spectrum The files should be numbered consecutively starting with a name such as location_target1 without extension The next target has to be named location_target2 etc For each target field three files will generated for example e location_targetl dat contains surface reflectance spectrum e location target1 txt contains target coordinates and processing parameters visibility water vapor column etc e location target1_dn1 dat contains the DN spectrum For a given location scene up to 9 targets can be extracted and used for the spectral calibration The geometry scene visibility and average water vapor content of the targets enter as parameters to the spectral calibration see Fig 5 73 The water vapor content has to be averaged from the values found in the location target txt files The first target DN file has to be selected by the user the remaining target files are automatically found provided the nomenclature with a consecutive numbering is applied The result of the spectral calibration are files with the spectral shifts per spectrometer and the new center wavelengths of all channels The spectral bandwidth of channels is not modified The DN spectra will be employed
108. where the h99000 indicates the symbolic height of 99000 m used for satellites the water vapor column content wv cm or g cm7 is scaled with 10 and the aerosol type is included in the last part of the name rol humidity bar 1 5 1 9 1 3 1 3 1 4 0 9 4 8 0 5 3 09 0 2 3 8 0 1 Table A 1 Altitude profile of the dry atmosphere Total ground to space water vapor content 0 41 cm 2 org cm 250 APPENDIX A ALTITUDE PROFILE OF STANDARD ATMOSPHERES 251 1 0 T7 3 0 4 5 70 20 8 0 1 8 1 2 0 7 0 4 Table A 2 Altitude profile of the midlatitude winter atmosphere Total ground to space water vapor content 0 85 cm or g cm Im DA 2 8 1 9 1 4 1 0 0 6 Table A 3 Altitude profile of the fall autumn atmosphere Total ground to space water vapor content 1 14 cm or g cm mbar C Yo g m Table A 4 Altitude profile of the 1976 US Standard Total ground to space water vapor content 1 42 cm or g cm APPENDIX A ALTITUDE PROFILE OF STANDARD ATMOSPHERES 252 Im 19 9 1 YO 6 0 10 4 2 65 Dal 60 1 7 53 1 0 Table A 5 Altitude profile of the subarctic summer atmosphere Total ground to space water vapor content 2 08 cm or g cm rel humidity m 76 66 55 45 39 31 Table A 6 Altitude profile of the midlatitude summer atmosphere Total ground to space water vapor content 2 92 cm or g em g
109. with factor 1000 Leaf area index LAI scaled with 1000 Fraction of photosynthetically active radiation FPAR scaled with 1000 Surface albedo integrated reflectance from 0 3 2 5 wm per cent 10 Absorbed solar radiation flux Rsolar Wm Global radiation E Wm omitted for constant visibility in flat terrain because it is a scalar which is put into the log file The next channels are only available in case of at least one thermal band Thermal air surface flux difference Riherm Ratm Rsur face Wm Ground heat flux G Wm7 Sensible heat flux H Wm7 Latent heat LE Wim 7 Net radiation Ry Wm Chapter 8 Sensor simulation of hyper multispectral imagery After atmospheric correction the surface reflectance and temperature emissivity cubes can be used to simulate new products which might be of interest e at sensor radiance cubes in the solar region 0 4 2 5 um for different solar geometries and atmospheric conditions e at sensor radiance cubes in the thermal region 8 14 um for different atmospheric conditions e g for satellite sensor studies e resampling of the surface reflectance cube to an existing or new multispectral sensor e resampling of the surface emissivity cube to an existing or new multispectral sensor This is a convenient way to simulate realistic data for a spaceborne version of an airborne instru ment to obtain radiance data under differenct
110. 0 50000000 0 50000000 31 North WGS 84 units Heters Select Input DEM Name Yerc_id atcor atcor_4 demo_data Hyspex_ATCOR_demo FL3_05m_sub_ele bsq UTM 1 1 593455 750 6283500 250 0 50000000 0 50000000 31 North 65 54 units Meterz F Upper Left Corner x 598455 750 yt 5283600 250 Use Reference File Number of Pixels xit E tut aot Define Output Name Basis 3 fsrc_id atcor atcor_4 demo_data Hyzpex_ATCOR_demo FL3_YMIR_geo_sub bsq select files Ruri Done Figure 5 6 Resize ATCOR input imagery 5 1 4 Select Input Image This function allows to select the basis input image i e the band sequential uncorrected image data in ENVI format It is useful to define the starting point including the default paths of the images for further processing 5 1 5 Import A small number of standard formats is supported for importing data layers to be used with ATCOR 3 Geo TIFF Multi Band geotiff in band ascending order RGBN Geo TIFF Geotiff in 4 band configuration storage order R G B N e g for photogram metric data CHAPTER 5 DESCRIPTION OF MODULES 313 NRGB Geo TIFF Geotiff in 4 band configuration storage order N R G B JPEG2000 Geo Import of JPEG2000 file extension jp2 with embedded georeferencing infor mation in geotiff or gml format ENVI BIP Image Imports and transforms an ENVI image in BIP band interleaved by pixel format to the ATCOR default BSQ band sequential format
111. 0 5 km Surface Temperature Error K Surface Temperature Error K E 1 Z T 4 E 1 z 3 4 Water Vapor cm Woter Yapar cm square Ts 2778 K diamond Ts 288 K triangle Ts 298 K square Ts 228 K diamond Ts 288 K triangle Ts 298 K Surface Temperature Error K Surface Temperature Error K US standard Atmosphere water Tof 1 K H 0 0 kn Us standard Atmosphere water Tloff 1 K H 0 5 km wie 0 3 1 0 1 3 2 0 faa ane 0 5 1 0 1 3 2 0 Z I Water apor cm Woter Yapar cm Figure 9 6 Surface temperature error depending on water vapor column water surface 1 00 0 99 Water Emissivity 0 98 0 397 10 0 10 5 11 0 11 5 12 0 12 5 13 0 Wovelength jem Figure 9 7 Spectral emissivity of water Symbols mark the TIRS channel center wavelengths CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 175 square Ts B4 K diamond Ts 294 K triangle Ta 304 K Tloffset 7 K Surface Temperature Error K Mid lotitude Summer Atmosphere e E10 e B11 0 95 H 0 0 ikm O 1 2 3 4 Water Vapor cm square Ts 2778 K diamond Ts 288 K triangle Ts 298 K Tiotfset 2 KE WS Standard Atmosphere e B10 e B11 0 95 H 0 0 km Surface Temperature Error K ane 03 1 0 1 3 2 0 faa Water Yapor cm Surface Temperature Error K Surface Temperature Error K square Ts 264 K diamond Ts 294 W triangle Ta 304 K Tloffset 3 K Mid lotitude Summer Atmospher
112. 000 X Generate Spectral Filter Functions V3 0 2015 Envi Header hdr or 3 Columns band number center wavelength bandwidth micron or nm Wavelength File bperion Atcor correct ion E01H1220642004125110PY_L1T_197band_subset hdr Red Gquss Select Type of Filter Function e weit Butterworth order 1 slow drop off ho w 2 1 Butterworth order 2 close to Gauss tl we 3 t Butterworth order 3 between Gauss rectangular we 4 Butterworth order 4 close to rectangular w 5 t Gauss m w 6 3 Rectangular sf t Triangular co Shape changes from near rectangular first bands to triangular last bands due to binning Spectral Binning Factor 2 Wavelength yarn we Figure 4 14 Supported analytical channel filter types across track FOV degree pixels per line dummy to agree with airborne ATCOR first last reflective band 0 35 2 55 um first last mid IR band 2 6 7 0 um first last thermal band 7 0 14 wm flag for tilt capability 1 yes 0 no required dummy Table 4 1 Example of a sensor definition file no thermal bands 4 7 Spectral smile sensors Imaging systems can employ different techniques to record a scene the whiskbroom design uses a rotating or oscillating mirror to collect an image line in across track direction with one or a few detector elements per spectral band The forward direction is provided by the motion of the platform Secondly a pus
113. 02174 0 00696 0 00625 0 00597 0 00417 0 00318 0 0290 0 00925 0 00830 0 00795 0 00556 0 00424 Table 9 3 Radiometric coefficients c1 for ASTER 9 6 12 DMC Disaster Monitoring Constellation DMC is a constellation of several orbiting satellites with an optical payload intended for rapid disaster monitoring All DMC sensors have three spectral bands green red NIR with a spatial resolution of 32 m and a swath of 600 km The metadata file dim and htm formats pertaining to each scene contains the solar geometry and the radiometric calibration coefficients The bias and gain specified in the metadata are defined as L bias DN gain 9 10 using the radiance unit Wm sr wm Since ATCOR uses the radiance unit mWem sr um and the equation L cotqDNn 9 11 CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 181 the calibration coefficients have to be calculated as co 0 1 x bias 9 12 c 0 1 gain 9 13 Note analysis of some DMC data from 2007 indicates that the specified bias in the NIR band is too high and better results are obtained if bias NIR 0 is employed 9 6 13 RapidEye The RapidEye constellation consists of 5 identical instruments in different orbits enabling a high temporal revisit time for any area The sensor has 5 multispectral bands covering the blue to NIR region with the specialty of a red edge band at 710 nm bandwidth 40 nm In addition the instruments can be t
114. 1 38 um or 1 88 um compare chapter 10 5 5 As a first approximation haze is an additive component CHAPTER 10 THEORETICAL BACKGROUND 217 to the radiance signal at the sensor It can be estimated and removed as described below Cloud areas have to be masked to exclude them from haze areas and to enable a successful haze removal The treatment of cloud shadow regions is discussed in chapter 10 5 6 Two de hazing algorithms are available method 1 is based on the haze thickness map 56 method 2 employs the haze optimized transform 105 Method 1 can be submitted from a GUI graphical user interface or as a batch job and performs the de hazing on the original digital number DN data This is an optional pre processing step to ATCOR An atmospheric correction can be performed as an independent next step to obtain surface reflectance data Method 2 performs a de hazing embedded in ATCOR so it is always combined with the atmospheric correction It is available in the GUI and batch modes 10 5 2 Haze removal method 1 This method automatically detects and removes haze in medium and high spatial resolution mul tispectral images The dark object subtraction algorithm is further developed to calculate a haze thickness map The haze thickness is computed for each spectral band excluding very bright ob ject because these can be misinterpreted as hazy areas The haze thickness for these bright object pixels is appoximated by a spatial interpolati
115. 10 O har od D G 0 3 1 0 12 1 4 od D G 0 3 1 0 12 1 4 Wavelength asr Wavelength asra Figure 2 3 Wavelength shifts for an AVIRIS scene angles were 41 2 and 135 8 Only part of the spectrum is shown for a better visual comparison of the results based on the original spectral calibration thin line and the new calibration thick line The spectral shift values calculated for the 4 individual spectrometers of AVIRIS are 0 1 1 11 0 88 and 0 21 nm respectively 2 3 Inflight radiometric calibration Inflight radiometric calibration experiments are performed to check the validity of the laboratory calibration For spaceborne instruments processes like aging of optical components or outgassing during the initial few weeks or months after launch often necessitate an updated calibration This approach is also employed for airborne sensors because the aircraft environment is different from the laboratory and this may have an impact on the sensor performance The following presenta tion only discusses the radiometric calibration and assumes that the spectral calibration does not change i e the center wavelength and spectral response curve of each channel are valid as obtained in the laboratory or it was already updated as discussed in chapter 2 2 Please refer to section 5 4 9 for further detail about how to perform an inflight calibration The radiometric calibration uses measured atmospheric parameters visibility or optical thickn
116. 10 THEORETICAL BACKGROUND 239 where arccos cos 6 cos 0 sin 6 sin 6 cos The angle 0 is the incident solar zenith angle 0 is the observation zenith angle and is the relative azimuth angle i e the difference between incidence and observation azimuth The extension of this volumetric kernel by Maignan is given as 1 1 kas ae E o 10 130 Kut 3 1 ae 10 150 The reciprocal Li Sparse kernel is used for the geometric part It is defined as l 1 1 cos Kgeg SIM Cost Y ra a A GeO i cos E 6 cos 7 2 cos 0 cos 6 where f acca eitant tang an cos in and cos cos 0 cos Or 10 6 4 BRDF cover index A continuous BRDF cover index BCI function is used for characterization of the surface It is calculated on the HDRF of four standard bands blue at 460nm green at 550nm red at 670 nm and near infrared at 840nm This reduced selection of spectral bands makes the index applicable for most current optical remote sensing systems The BCI function characterizes the image based on intrinsic BRDF properties from strong forward scatterers water to neutral targets asphalt to backward scatterers soils and vegetation types The index implementation is using the normalized difference vegetation index NDVI as a first input for vegetation density quantification due to its known relation to the leave area index LAI which has a significant influence on t
117. 160802438555 2009 34 Hay J E and McKay D C Estimating solar irradiance on inclined surfaces a review and assessment of methodologies Int J Solar Energy Vol 3 203 240 1985 35 Hu B Lucht W Li X and Strahler A H Validation of kernel driven semiempirical models for the surface bidirectional reflectance distribution function of land surfaces Remote Sens Environ vol 62 no 3 pp 201214 1997 36 Huete A R A soil adjusted vegetation index SAVI Remote Sensing of Environment Vol 25 295 309 1988 37 Idso S B and Jackson R D Thermal radiation from the atmosphere J Geophysical Research Vol 74 5397 5403 1969 co e Isaacs R G Wang W C Worsham R D and Goldberg S Multiple scattering LOW TRAN and FASCODE models Applied Optics Vol 26 1272 1281 1987 Jimenez Munoz J C and Sobrino J A Atmospheric water vapour content retrieval from visible and thermal data in the framework of the DAISEX campaign Int J Remote Sensing Vol 26 3163 3180 2005 09 E 40 LL Kahle A B et al Middle infrared multispectral aircraft scanner data analysis for geological applications Applied Optics Vol 19 2279 2290 1980 Kamstrup N and Hansen L B Improved calibration of Landsat 5 TM applicable for high latitude and dark areas Int J Remote Sensing Vol 24 5345 5365 2003 41 Lo 49
118. 185 Start threshold T1 0 05 as Mask DDV reference pixels 0 01 lt p ref SWIR lt T1 4 Increase threshold T1 za me T1 lt T2 e po r upto T2 wA 47 gt Constant scene VIS 23 km Apply DDV p red 0 5 p 2 2 um Calculate VIS from intersection of modeled L red with measured L red Number of negative p NIR pixels q A wa es gt 1 ofscene pixels lt SY gt Increase VISupto 80 km n a and VIS lt 80 km Calculate visibility index vi amp AOT maps Non ref pixels are assigned the average VIS ref or spatially interpolated values Figure 10 2 Visibility AOT retrieval using dark reference pixels 10 1 Basics on radiative transfer This chapter presents the basic concepts and the terminology The full set of equations is docu mented here as implemented in ATCOR We start with the radiative transfer equation in the solar spectral region 0 4 2 5 ym for a flat terrain under clear sky conditions First the equation for an infinite plane of uniform reflectance is presented Then the case of a small uniform surface embedded in a large homogeneous background of different reflectance is discussed We continue with the rugged terrain and finally discuss the equations for the thermal spectral region 8 14 um 10 1 1 Solar spectral region For a cloud free sky and a uniform ground of reflectance p the radiance signal received at the sensor consists of scattered solar radiati
119. 2 p green lt 0 5 10 72 e medium snow ice probability coded 60 same as for low probability but with a more strin gent NDSI threshold of 0 6 This is the snow assignment in the hcw bsq file p blue gt 0 22 and NDSI gt 0 6 and DN blue lt Tsaturation 10 73 If no blue band exists a green band is used as a substitute If a green band and a SWIR2 band exist the rules are DN blue lt Tsaturation and p blue gt 0 22 and NDSI gt 0 6 or p green gt 0 22 and p SWIR2 p green lt 0 3 10 74 This is very similar to the snow assignment in the hcw bsq file except for the threshold for p SWIR2 p green e high snow ice probability coded 90 same as for medium probability but with a more stringent NDSI threshold of 0 7 p blue gt 0 22 and NDSI gt 0 7 and DN blue lt Tsaturation 10 75 If no blue band exists a green band is used as a substitute Again if a green band and a SWIR2 band exist the rules are DN blue lt Tsaturation and p blue gt 0 22 and NDSI gt 0 7 or p green gt 0 22 and p SWIR2 p green lt 0 2 10 76 CHAPTER 10 THEORETICAL BACKGROUND 208 10 4 Standard atmospheric conditions Standard conditions comprise scenes taken under a clear sky atmosphere This means the visibility aerosol optical thickness can be assumed as constant over a scene or it might vary within a certain range excluding haze and a visibility map can be calculate
120. 3 5 Dak TENI Das Menu 041 E TEE 5 4 4 TOR 5 4 6 5 4 7 5 4 8 5 4 9 5 4 10 5 4 11 04 12 5 4 13 Menu Deed ae peo Menu 5 6 1 Dd 5 6 3 5 6 4 9 6 9 9 6 6 5 6 7 5 6 8 Menu Ds Fok dad Ds dd 014 Menu 4 BORO ne tae eee hE RHR Ee eee eee eee te eee eee ee 60 Define Sensor Parameters 2 1 a a a a a 61 Generate Spectral Filter Functions 0 0 0 2 00 ee eee 62 Apply Spectral Shift to Sensor 63 DOGALO Blackbody Facog o esos 2 ew eed e ow ee ee ES 64 RESLUT Resample Atm LUTS from Database 65 IO aa a AAA BERS ERS 67 DECI IDO canoas ass 67 DEM Preparation eo ociosa ca asas a 68 Gloro ABD N nc anno dra AAA 69 sland o III 70 Cast Shadow Mask e 2 71 Image Based Shadows a fall PEM Sr ne sce ee ae Oo ORG Bee Be ee eG Ges 13 Quick Topographic no atm Correction 2 0 00 74 ATEN 0 or seras rra 76 The ATCOR main pamel 76 ATCOR2 multispectral sensors flat terrain 76 ATCOR3 multispectral sensors rugged terrain 2 004 76 ATCOR2 User defined Sensors 2 0 a a a TT ATCOR3 User defined Sensors a a a 17 SPECTRA WCW o sss saa pean tae eee weeds Ce ee eee HOS 80 A III 81 Visibilty Estimate ooo aaa A 81 Inflight radiometric calibration module 81 Shadow removal panels 84 Panels for Image Processing 2 a a 87 Start
121. 3 consists of two components atmospheric backscattering and volume scattering which are combined into one component in Fig 2 2 to obtain a compact description Only radiation component 2 contains information from the currently viewed pixel The task of atmospheric correction is the calculation and removal of components 1 and 3 and the retrieval of the ground reflectance from component 2 So the total radiance signal L can be written as L Lpath T Lreflected as Laa Li L2 L3 2 6 The path radiance decreases with wavelength It is usually very small for wavelengths greater than 800 nm The adjacency radiation depends on the reflectance or brightness difference between the currently considered pixel and the large scale 0 5 1 km neighborhood The influence of the adja cency effect also decreases with wavelength and is very small for spectral bands beyond 1 5 um 74 For each spectral band of a sensor a linear equation describes the relationship between the recorded brightness or digital number DN and the at sensor radiance Fig 2 2 L co c DN 2 7 The co and c are called radiometric calibration coefficients The radiance unit in ATCOR is mW cm is sr yum For instruments with an adjustable gain setting g the corresponding equation Teur DN 2 8 g CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 18 Figure 2 2 Schematic sketch of solar radiation components in flat terrain L path radiance
122. 9 bands where band 9 is the surface temperature calculated with the split window method see below and assuming an emissivity of 0 98 for the thermal bands The conversion of the Landsat 8 TIF files into a wavelength ascending ENVI band sequential bsq file can also be performed as a batch job e 8_oli_envi tifname Here tifname is the complete name of the first band of the Landat 8 TIF files including the folder Example 18_oli_envi data1 scene_B1 TIF Then a layer stacked ENVI bsq file for the OLI bands is created 8 channels without panchro matic named datal scene OLI bsq e landsat amp _envi tifname Here tifname is the complete name of the first band of the Landat 8 TIF files including the folder Example landsat8_envi data1 scene_B1 TIF Then a layer stacked ENVI bsq file for the OLI TIRS bands is created 10 channels without panchromatic named datal scene_ OLITIRS bsq The Read Sensor Meta Data button below the File button on ATCOR s top level menu should be used to generate the corresponding cal and inn files The cal file is already with increasing band center wavelength i e the cirrus band is at channel position 6 and the SWIR1 SWIR2 bands at position 7 8 respectively If the scene is named scene_xxx bsq then the xxx inn file generated by the meta file reader has to be renamed as scene_xxx inn before starting ATCOR
123. A ratio with an exponential fit function for the water vapor 216 WO Haze removal method ce Pt RE RB Yee ER tadik GER EE Oe 218 10 18Subset of Ikonos image of Dresden 18 August 2002 219 10 19Haze removal over water ALOS AVNIR2 0 2 0 0 00000 eee 220 10 20Scatterplot of apparent reflectance of cirrus 1 38 wm band versus red band 222 10 215ketch of a cloud shadow geometry 2 2 e 223 10 22F low chart of processing steps during de shadowing 4 224 10 23Normalized histogram of unscaled shadow function 220 10 24Cloud shadow maps of a HyMap scene 2 2 ee 226 10 25De shadowing of a Landsat 7 ETM scene 0000 eee eee 229 10 26Nadir normalization of an image with hot spot geometry 231 10 27Geometric functions for empirical BRDF correction Left Functions G eq 10 123 for different values of the exponent b Right Functions G of eq 10 123 for b 1 and different start values of Gr The lower cut off value is g 0 2 233 10 28BRDF model calibration scheme 0 2 ee eee eee ee ens 236 10 29Image correction scheme 6 666 baw ebm ER Oe ee e a 237 10 30BREFCOR mosaic correction Top uncorrected Bottom corrected RapidEye chessboard image mosaic e DLR 2 2 002 eee ee ee 239 10 31 Weighting of q function for reference pixels 1 2 a ee ee 240 List of Tables 4 1
124. APEX_2015_L1 dat elect Input Smile File poly_ord4 src_idl atcor atcor_4 sensor 4PEX_DoubleFuHt_SH sni le dat Define New Sensor Name Directory sro_idl atcor atcor_4 sensor APEX_ 224 SH output sensor defined APEX_2 5FuHM_SH Help Apply Smile Apply FWHM Done ELE LE Figure 5 16 Application of spectral shift to sensor 5 2 4 BBCALC Blackbody Function This routine calculates the blackbody function as described in section 10 1 5 weighted by the spectral response curve of the thermal band used for the temperature retrieval compare Fig DLE Inputs CHAPTER 5 DESCRIPTION OF MODULES 65 Spectral response file Select the rsp file of the spectral band in the thermal IR to be used for temperature retrieval Exponential Fit Limits The lower and the higher limit of the temperatures for which a fitting function should be created Unit of radiance output Select the unit either per micron or without normalization Outputs A file _hs bbfit is created containing the fitting parameters AAA A BBCALC Blackbody Calculations Spectral response File fsrc_idl atcor atcor_23 sensor aster14_hs aster14 rsp Exponential Fit of Planck Function Tbb 1 Z a b In Lbb Low temperature T1 Kelvin 270 0 High temperature T2 Kelvin 30 0 A Spectral radiance L mll m2 sr micron Required for ATCOR vv In band radiance L l cm2 sr For general purpose only Run bbeale running coeffi
125. Atmospheric Topographic Correction for Satellite Imagery ATCOR 2 3 User Guide Version 9 0 0 June 2015 R Richter and D Schlapfer DLR German Aerospace Center D 82234 Wessling Germany ReSe Applications Langeggweg 3 CH 9500 Wil SG Switzerland DLR IB 565 01 15 The cover image shows a Landsat 8 OLI subset from France path row 196 29 latitude 44 5 longitude 5 4 acquired July 14 2013 Top original scene RGB 660 560 443 nm middle haze removal with new algorithm bottom haze removal with previous algorithm The new algorithm achieves better dehazing results ATCOR 2 3 User Guide Version 9 0 0 June 2015 Authors R Richter and D Schlapfer 1 DLR German Aerospace Center D 82234 Wessling Germany 2 ReSe Applications Langeggweg 3 CH 9500 Wil SG Switzerland All rights are with the authors of this manual Distribution ReSe Applications Schlapfer Langeggweg 3 CH 9500 Wil Switzerland Updates see ReSe download page www rese ch software download The ATCORO trademark is held by DLR and refers to the satellite and airborne versions of the software The MODTRAN trademark is being used with the express permission of the owner the United States of America as represented by the United States Air Force Contents 1 Introduction 12 2 Basic Concepts in the Solar Region 15 2 1 Radiation components does aaa aa 17 Za FCC CAIN te ee a aaa EE a E eRe es 20 2 3 Inflight rad
126. Azimuth solar angles zenith azimuth for this data set and optionally the flight altitude above ground airborne ATCOR version only A combined index is created based on NIR brightness and two red blue and green blue indices which results in a scaled number between 0 for full cast shadow and 1 for full illumination which is proportional to the cast shadow 5 3 7 DEM Smoothing Smooth a DEM or any other single band image in order to remove artifacts in the atmospherically corrected imagery All related DEM layers are automatically smoothed as well e g slope aspect skyview Alternatively this task could be done with any image processing software CHAPTER 5 DESCRIPTION OF MODULES 714 Inputs Input DEM File Name Usually a DEM x ele bsq is selected here but any other single band ENVI image or the x_ilu bsq file is also accepted The routine searches automatically for related files i e _sky _slp and or _asp and smoothes them with the same parameters Diameter of DEM Filter Size of filter box in pixels diameter Output Name Name of Elevation file output auxiliary layer names will be derived from that Outputs ENVI file s smoothed or filtered by the given factor and method Actions Smooth uses the standard smoothing i e lowpass filter in the spatial domain Median uses a median filter for data correction e g to remove noise or outliers from the DEM ATTENTION The xilu file is not smoothed automatic
127. Buildings ooooommmmmrror Q Yes No Shadow Removal Clouds Buildings ssessererrreresesseni Q Yes No valua Added PIRCICILIGC ES aaee AAD EA AAE EEEE ES AE y Yes No Value Added Products icccsssccsssacascesecseeseessteses Vv Yes No ds ree ete Yes Q No Solar lol at al sscorcococonocon cono cono no conos y Yes No SEA E Bal sonocororonconcccococosonconnogose vy Yes No Cancel OK Cancel OK Figure 5 41 Image processing options Right panel appears if a cirrus band exists Options that are not allowed for a specific sensor will appear insensitive If the haze removal option is selected in combination with Variable Visibility the visibility index proportional to total optical thickness map is coded with the values 0 182 The value visindex 0 corresponds to visibility 190 km each integer step of 1 corresponds to an AOT increase of 0 006 The array serves as a fast method of addressing the radiative transfer quantities transmittance path radiance etc in case of a spatially varying visibility i e in combination with the DDV algorithm IF the Haze or Sunglint Removal button is selected the next panel will ask for haze removal over land option 1 haze or sunglint removal over water option 2 or haze removal over land and water option 3 In case of thermal bands an emissivity selection panel will appear wv Constant scene emissivity 0 9800 band 13 at 10 661 micron v Emissivitiest water vegetation e 0 98
128. CVR method see chapter 10 1 5 chth_w2 right window channel chth_al left absorption channel chth_a2 right absorption channel line 32 e_water e_veget e_soil e_sand surface emissivities adjusted NEM channel with Tmax line 38 0 iwv_model water vapor retrieval 1 no band regression 2 band regression The choice iwv_model 0 indicates the water vapor retrieval is disabled Option 1 means the water vapor retrieval is performed for the selected bands and in case of several measurement bands the one with the smallest standard deviation is selected per 940 and 1130 nm region Finally if both regions are active the average of the water vapor maps of both regions is taken Option 2 employs a linear regression of bands which yields better results for the water vapor map if the data is noisy or not accurately calibrated If iwv_model 2 and channels in the 940 nm and 1130 nm regions are specified then only the 940 nm region is evaluated with a linear regression of bands If the regression is intended for the 1130 nm region then the 940 nm channels all channels in line 29 have to be specified as 0 line 34 0 icirrus flag for cirrus removal 0 disabled 1 enabled 1 forced The value icirrus 1 enforces cirrus detection and removal i e termination criteria are ignored line 35 0 irradO flag for solar flux on ground 0O disabled 1 enabled For irrad0 2 the surface reflected leaving radiance is calculated
129. F corrected data as indicated by the dotted lines of figure 4 9 The polishing requires hyperspectral imagery Finally a classification may be performed Figure 4 10 shows the input output image files associated with ATCOR processing On the left part the flat terrain case is treated on the right part the rugged terrain case In mountainous terrain the DEM DEM slope and aspect files are required Optional input are the skyview file and the shadow map the latter can also be calculated on the fly The slope and aspect files can be calculated from ATCOR s interactive menu or run as a batch job slopasp_batch see chapter 6 3 The skyview file has to be computed with the skyview program see chapter 5 3 4 4 4 Directory structure of ATCOR Figure 4 11 shows the directory structure of the satellite version of ATCOR There are a num ber of sub directories with the following content The bin directory holds the ATCOR program with all modules as listed in chapter 5 The cal directory holds all supported satellite sensors in sensor specific sub directories The sensor definition is contained in two files one contains the extraterrestrial solar irradiance e g e0_solar_aster spc the second one contains the radiomet ric calibration coefficients e g aster cal The atm_lib contains the results of the atmospheric database after resampling with the sensor specific spectral response curves The spec_lib is a di rec
130. FICS 180 nominal value of Lmin is zero For each sensor AWiFS Liss 3 Liss 4 the calibration coefficients seem to be constant with time i e independent of the scene based on laboratory calibration The radiometric coefficients for ATCOR s cal file have to be calculated as e co Lmin and cy Lmax Lmin b where b 1023 for AWiFS 10 bit data encoding and b 255 for Liss 3 and Liss 4 8 bit encoding The analysis of a couple of scenes showed that a non zero bias cy is required to obtain reasonable surface reflectance spectra Therefore typical average bias values are included in the standard cal file for each sensor A fine tuning of the calibration coefficients may be necessary to obtain better agreement between scene derived surface reflectance spectra and library or ground measured spectra 9 6 11 ASTER ASTER has 9 reflective and 5 thermal bands ATCOR calculates surface reflectance and a surface brightness temperature from band 13 ASTER has four gain settings high H normal N and lowl L1 low2 L2 for the reflective bands Table 9 3 contains the cl values for the different reflective bands and gain settings in the ATCOR radiance unit mWem sr yum It was taken from the ASTER user s guide 21 The thermal band 13 has a calibration gain of cl 5 693E 4 high gain normal gain Tow gain 1 Tow gain 2 1 2 3 4 5 6 Y 8 9 0 01087 0 00348 0 00313 0 00299 0 00209 0 00159 0
131. HEG CREE Re EHS 154 Deed HOOT eco prados pases nana S SEEGER Bee ees 155 Build AOR sar AAA ES 156 Bda DUES ODO lt lt cro cios raro eect bene He Se oe HH HES SS 157 9 3 Preference parameters for ATCOR 2 a a a a a 158 9 4 Job control parameters of the inn fle 0 0 000000 00 161 Go Froblems and HMS 6 eee tae han bo ee eA we RE SS EER ee eR HG 168 9 6 Metadata files geometry and calibration o 170 9 6 1 Landsat 7 ETM Landsat 5 TM 0 02 02 0004 170 Oe Ae nt i eh wee ww OE Ow ERA 171 OOo Landas TIRS 2243446466848 Seabee eee ee wee EES 172 064 APOLLO SPOTS 24 eedee be eee bbe eee ara 176 OMe POTO ae tebe et bedude oes Ada AAA 177 9 6 6 ALOSAVNIR 2 644464 664 82 AKER EE HED ERE ewe ee EE eS 178 Oe MIMI cr aora GEER HEBER EERE ORE eS 178 UO SURO III 178 OA Ii ARRIETA 179 CONTENTS SOHO MO acarrea A A ee OJD ASE not kw eee Reh AAA 9 6 12 DMC Disaster Monitoring Constellation AS FOG o heed ee hee e eH he wd Doli A no eee Re ee ER Ow See ee E AD VORIIV IEW soso bee ys bate Ge ee ee eS EEEG REY ER HES BAGS PAN nc ka EERE AAA IAEA AA OAT ERA 6 fg eB Ge eee he eh ee AAA Ge eG 10 Theoretical Background 10 1 Basics on radiative transfer ooo a e a 10 1 1 Solar spectral region 4 s sas ok ewe we Ow eee Bw eee Ow ew es 10 1 2 Illumination based shadow detection and correction 10 1 3 Integrated Radiometric Correction IRC
132. Lo Kaufman Y J and Sendra C Algorithm for automatic atmospheric corrections to visible and near IR satellite imagery Int J Remote Sensing Vol 9 1357 1381 1988 YS E Kaufman Y J et al The MODIS 2 1 um channel correlation with visible reflectance for use in remote sensing of aerosol IEEE Transactions on Geoscience and Remote Sensing Vol 35 1286 1298 1997 References 246 44 Kleespies T J and McMillin L M Retrieval of precipitable water from observations in the split window over varying surface temperature J Applied Meteorology Vol 29 851 862 1990 45 Kobayashi S and Sanga Ngoie K The integrated radiometric correction of optical remote sensing imageries Int J Remote Sensing Vol 29 5957 5985 2008 46 Kobayashi S and Sanga Ngoie K A comparative study of radiometric correction methods for optical remote sensing imagery the IRC vs other image based C correction methods Int J Remote Sensing Vol 30 285 314 2009 47 Irish R R Barker J L Goward S N and Arvidson T Characterization of the Landsat 7 ETM automated cloud cover assessment ACCA algorithm Photogr Eng Remote Sens Vol 72 1179 1188 2006 48 Yi C Y Haze reduction from the visible bands of Landsat TM and ETM images over a shallow water reef environment Remote Sens Environm Vol 112 1773 1783 2008 49 Krause K Radian
133. MODULES 36 lrequarcy o a rad iran d shadamw function irearmuliFed eoged shadow function ori ba al histani rerig a AA A Es 1 0 40 5 qn o wAktelad ahador Funchon a el a m Figure 5 40 Quicklook of de shadowing results Top left histogram of PhiU threshold 0 15 range 0 40 iteration 1 Top right histogram of PhiU threshold 0 38 range 0 19 iteration 2 Center results for iteration 1 left to right original shadow mask de shadowed image Bottom results for iteration 2 CHAPTER 5 DESCRIPTION OF MODULES 87 5 4 11 Panels for Image Processing When pressing the button IMAGE PROCESSING in one of the main panel figure 5 31 some additional panels will pop up First the processing options are to be selected see figure 5 41 Blocked Options Are Not Available For The Selected Sensor Might also apply for a reduced set of bands Either Haze or Cirrus Removal not both Blocked Options Are Not Available For The Selected Sensor Variable Visibility aerosol optical thickness 9 Yes No Might also apply for a reduced set of bands Variable Visibility aerosol optical thickness Yes No variable Water VAPON scccccovscesacusaeeeateusesisavcans Yes No EPG ae LEER Hae 5 onocsuospue Subba doo o0oonboenoanueE V Yes ORNs jes ble E BMS ECE conencooconooenoccncasneneaces y Yes No Haze le SI El a anaaancnnoanncancoaaKoNGGangas Yes No Shadow Removal Clouds
134. ODULES 62 First last Mid IR Band Band numbers starting at one none 0 First last Thermal IR Band Band numbers starting at one none 0 Applied scaling factor Enter the constant scaling factor which had been applied to the data to fit it to an integer data type from mW cm srum Typical values are 100 500 or 1000 The x cal file will be created with constant values for all bands according to this value If the scaling factor is not constant the x cal file is to be edited manually NOTE a scale factor of zero or below inhibits to write a cal file Calibration Pressure absolute pressure in hPa of instrument during spectral calibration in lab oratory Instrument Pressure pressure of instrument during operations Absolute pressurized instrument with constant pressure as of this value Relative pressure difference to in flight ambient pressure in hPa value of 0 hPa is ambient pressure Thermal Sensor Temperature Band Number Spectral band used for temperature retrieval algorithm Optional according to sensor type Smile Sensor Smile Sensor Response Type required for convolution when sensor defini tion is not given explicitly may be Butterworth Order 1 slow drop off Butterworth Order 2 close to Gauss Butterworth Order 3 between Gauss Rect Butterworth Order 4 close to Rectangular Gaussian Rectangular Triangular Decreasing Binning from Rectangular to Triangular or
135. PTER 5 DESCRIPTION OF MODULES 84 5 4 10 Shadow removal panels The interactive session of the de shadowing method enables the setting of three parameters that influence the results compare Figures 5 38 5 39 1 a threshold Pr for the unscaled shadow function PhiU to define the core size of the shadow regions see chapter 10 5 6 for details 2 the maximum range Puna for re scaling the unscaled shadow function PhiU into the 0 1 interval of the scaled shadow function 3 the last parameter sets the minimum value of the scaled shadow function Phis typically in the range PhiS 0 02 0 10 i e the darkest shadow pixels of the scene are treated as being illuminated with a fraction PhiS of the direct solar irradiance histogram 1 0 i 0 8 0 5 0 4 0 2 a cone OO Ls 1 0 0 5 OG 0 5 1 0 unscaled shadow function Figure 5 38 Normalized histogram of unscaled shadow function The first two parameters are most important The last parameter is not very critical and a default value in the range PhiS 0 04 0 08 covers most cases of interest The linear type of re scaling of PhiU is recommened The exponential option exaggerates the de shadowing results but it can be used to enhance the trends for a quick visual inspection When pressing the button Run Interactive De Shadowing a reduced size image of the original scene the shadow mask and the de shadowed image will pop up with the histo
136. RF S the bottom of atmosphere directional reflectance after standard ATCOR 4 atmospheric compen sation as described above Sample result The BREFCOR correction is most appropriate for wide FOV sensors i e with FOV values larger than approx 20 degrees typically found for airborne imagery For satellite imagery the BREF COR correction is typically applied for mosaicking a number of images acquired in the same area at varying illumination and sensor observation angle conditions A sample result for RapidEye imagery is displayed in Figure 10 30 The image is a chessboard of a false color composite of two scenes acquired with a relative observation angle difference where the first had a observation zenith of 1 4 and a solar zenith of 18 7 whereas the second scene was 8 days later and had angles of 14 7 and 14 3 respectively The lower image is the correction result based on the calibrated Ross Li sparse BRDF model Some of the BRDF effects can be removed by this method as long as the image statistics are sufficient However not all effects can be fully removed this could also be attributed to changing atmospheric conditions between the two dates 10 7 Summary of atmospheric correction steps Although the case of a flat terrain could be treated as a special case of a rugged terrain with the same elevation everywhere this is not an efficient solution because the rugged terrain algorithm runs 3 to 4 times slower than the flat t
137. TER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 152 These files have to be resampled with the sensor specific channel filter curves The file names for the solar region include the altitude the aerosol type and the water vapor content They have the extension atm Example h99000_wv04_rura atm represents a file with the symbolic altitude 99 000 m water vapor column 0 4 cm and the rural aerosol Each atm file contains the look up tables for the visibility range 5 120 km solar zenith angles 0 70 and ground elevations O 2500 m increment 500 m The files phasefct bin in the atcor bin directory contain the path radiance as a function of the scattering angle for the standard multispectral instruments with tilt capability up to 50 off nadir e g SPOT Ikonos Quickbird ALOS AVNIR 2 A small field of view is assumed for all hyperspectral or multispectral instruments i e the specified tilt angle and solar zenith angle holds for the whole scene 9 1 3 Database update with solar irradiance In the solar region any high spectral resolution database of LU T s is based on the specification of an extraterrestrial spectral solar irradiance because the values of path radiance direct and diffuse solar fluxes depend on solar irradiance Other quantities direct and diffuse atmospheric transmittances and spherical albedo are independent of the solar spectrum ATCOR s standard atmospheric data
138. TF and shifts the phase of the input spatial frequencies Usually only the MTEF is given as a figure of merit or its counterpart the point spread function PSF The MTF is the 2 D Fourier transform of the PSF 91 CHAPTER 5 DESCRIPTION OF MODULES 120 N CONVERT_ATMLIB Convert atm Files in atm_lib for Another Solar Irradiance File Versio Selected SENSOR ASTER14_HS Select high resolution irradiance e0_solar_kurucz2005_04nm dat RUN Figure 5 76 Convert atmlib to new solar reference function Fig 5 77 presents the GUI for the MTF PSF evaluation The image is loaded in the left part The user should click with the left mouse button in the area where the MTF evaluation is intended This area will be shown in top middle part of the GUI Then the MTF can be evaluated for a line or an edge yielding the line spread function LSF or edge spread function ESF respectively The user is responsible to select an appropriate feature by clicking the start and end points of the line with the left and center mouse buttons respectively After clicking Calculate MTF the MTF is plotted below for up to four fixed channels as well as the effective GIFOV Ground Instantanous Field of View defined as one half of the reciprocal of the spatial frequency at which the MTF is 0 5 The effective GIFOV is also called effective instantaneous field of view EIFOV and it is specified in pixels Low values of the EIFOV represent
139. TICAL BACKGROUND 184 if blue band exists Masking haze clear cloud water shadow and ratio blu red gt 0 Water Vapor Map wv if required bands exist Update LUT LUT wv cirrus removal haze removal lterative reflectance retrieval incl adjacency and spherical albedo lt a th Pa AE in a A i i A aha Spectral polishing removal J d J BRDF correction DDV algorithm VIS map bands Red SWIR or Red NIR visibility index vi amp AOT Figure 10 1 Main processing steps during atmospheric correction enough reference pixels are found then a constant VIS 23 km is used otherwise the visibility for each reference pixel is determined as the intersection of modeled and measured radiance in the red channel Then the NIR channel is checked concerning negative reflectance pixels mainly water shadow If the percentage of negative reflectance pixels is higher tahn 1 of the scene pixels then the visibility is iteratively increased up to 80 km Finally the visibility index and AOT 550nm are calculated and the nn reference pixels are assigned the average visibilty or optionally a spatial triangular interpolation can be performed If the aerosol type rural urban maritime desert is not fixed by the user the flow chart is executed for these four types and the type closest to the scene estimated type is used see chapter 10 4 2 for more details CHAPTER 10 THEORETICAL BACKGROUND
140. The panel as displayed in Fig 5 14 allows the below options 000 x ATCOR Sensor Definition Selected Sensor 1 atcoriatcor_4sensorraviris98_demo sensor_awirisz98_demo dat Sensor Type Standard sv Smile Sensor ys Thermal Sensor Sensor Total FOY deg 1 2000 Humber of Across Track Pixels 14 First last Reflective Band First last Hid IR Band EJ E E as yt Hegy ct a gt a E E or First last Thermal IR Band Applied scaling factor from mll cm2 sr microns 500 000 Calibration Pressure hPal 013 000 Instrument Pressuret w Absolute Relative 1 p 000000 Sensor data loaded from src_idl atcorrfatcor_4sensorraviris98_demo zensor_awiris98_demo dat Help New Sensor Delete Update Sensor Rename Done Figure 5 14 Definition of a new sensor Sensor Selection Select any of the already defined sensors from within ATCOR a sensor is selected by its sensor definition file sensorx dat within a sensor directory Use the function New Sensor in case your sensor has not yet been defined Inputs Sensor Type This is to be selected first smile sensor and thermal sensors require additional inputs as displayed in the panel Sensor Total FOV deg edge to edge FOV in across track direction in degrees Number of Across Track Pixels Nominal number of pixels in the unrectified data First last Reflective Band Band numbers starting at one none 0 CHAPTER 5 DESCRIPTION OF M
141. This chapter contains a description of the concepts and equations employed for the atmospheric correction We start with the basic equations in the solar and thermal spectral region for clear sky conditions standard case then move on to non standard conditions comprising bidirectional reflectance BRDF effects hazy scenes and a treatment of shadow areas caused by clouds or buildings Stan dard atmospheric conditions include the option of a constant visibility aerosol optical thickness and water vapor content per scene as well as the retrieval of a visibility and water vapor map if the required spectral bands are available for the specific sensor Water vapor correction on a pixel by pixel basis is usually necessary for hyperspectral imagery The section on the non standard conditions contains a short discussion on empirical correction methods for bidirectional effects It continues with the description of a statistical haze removal method The third section presents a technique to compensate shadow effects i e cloud or building shadow areas are masked and de shadowed Then an overview is presented of all major processing steps involved in the atmospheric correction After atmospheric correction the surface reflectance cube can be used for classification A simple automatic method is included here based on template reflectance spectra of different surface covers Finally the accuracy of the atmospheric correction is discussed Before going
142. ULES 100 intrinsic reflectance unit or the percent range 0 100 Figure 5 58 shows the graphical user interface The input spectrum is high spectral resolution data The result has the same format and dimensions as the input file 600 Xx Low Pass Filter a Spectrum Tool for smoothing noisy hyperspectral target spectra during INFLIGHT CALIBRATION Pick Input Spectrum a Vsro_idl atcor atcor_4 spec_lib daedalus02 alfalfa_da dat Output Filename low pass filtered spectrum src_idl atcor atcor_4 spec_lib daedalus02 alfalfa_da_filter3 dat Low pass filter size number of channels RUN Low Pass Filter Status 1 Quit Figure 5 58 Low pass filtering of a reflectance spectrum 5 6 3 Spectral Polishing Statistical Filter Remove spectral artifacts in high spectral resolution imaging spectroscopy data Inputs Input file name A hyperspectral image cube usually the output of atmospheric correction _atm bsq Sensor Spectral Response Defines the first band of the sensor response x rsp This field may be left empty in that case the wavelength information from the ENVI header is used if the wavelengths tag is properly given if no wavelength reference is there a spectrally equidistant succession of the spetral bands is assumed Note the Savitzky Golay filter is not wavelength aware and uses always the assumption of equidistant constantly increasing bands Number of polishing bands on each side Adjac
143. a rugged terrain If the keyword elev is specified the simulation is always performed for a flat terrain regardless of any possible DEM file names in the inn file e sz 35 5 an example of a solar zenith angle of 35 57 e vis 25 an example of a visibility of 25 km e pixelsz 4 5 an example of a pixelsize of 4 5 m CHAPTER 8 SENSOR SIMULATION OF HYPER MULTISPECTRAL IMAGERY 149 e adjrange 500 an example of an adjacency range of 500 m e scalef 10 000 scale factor for the TOA radiance The default is scalef 1 0 which provides the output file as float data of TOA radiance in units of mWem sr um If scalef gt 1 e g scalef 10 000 the output TOA radiance is stored as 16 bit unsigned integer multiplied with the scale factor The advantage is a smaller output file compared to the 32 bit float the drawback is that radiances will be truncated at 65 000 which might happen for bright surfaces e g snow vegetation in the NIR with scalef 10 000 see Figure 8 4 Therefore the easiest way to avoid scale problems is to use the default scalef 1 0 and have a float radiance output cube sz 30 deg olbedo 4 3 9 4 0 03 1 0000 700 010 Radiance mw cm ert um AA 0 3 1 0 ile ZO o Wavelength san Figure 8 4 TOA radiances for three albedos and a solar zenith angle of 30 MODTRAN calculation for a mid latitude summer atmosphere rural aerosol visibility 15 km ground elevation 500 m above sea level
144. a temperature offset of 2 K in equation 9 8 is required to keep errors smaller than 1 K in most cases in the shaded areas Higher errors are encountered for H 0 sea level if the surface temperature is 10 K below the air temperature CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 173 square Ts B4 K diamond Ts 294 K triangle Ta 304 K Surface Temperature Error K Mid lotitude Summer Atmosphere e E10 e B11 0 98 H 0 0 ikm O 1 2 3 4 Water Vapor cm square Ts 2778 K diamond Ts 288 K triangle Ts 298 K Surface Temperature Error K US Standard Atmosphere e 819 e B11 0 98 H 0 0 km ane 03 1 0 1 3 2 0 fad Water Vapor cm Surface Temperature Error K Surface Temperature Error K square Ts 264 K diamond Ts 294 W triangle Ta 304 K Mid latitude Summer Atmosphere e B10 e B11 0 98 H 0 5 km 1 2 3 4 Woter Yapar cm square Ts 228 K diamond Ts 288 K triangle Ta 2 8 K US Stondard Atmosphere e 8109 e B11 0 98 H 0 5 km 0 3 1 0 1 9 2 0 fad Woter Yapar cm Figure 9 5 Surface temperature error depending on water vapor column emissivity 0 98 CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 174 square Ts 264 K diamond Ts 294 K triangle Ta 304 K square Ts 264 K diamond Ts 294 W triangle Ta 304 K Mid lotitude Summer Atmosphere water Tlaffi 1 Kk H 0 0 kmj Mid latitude Summer Atmosphere water Tlefff 1 K H
145. aced The scatterplot is computed in terms of the apparent TOA or at sensor reflectance Of P1 38 Versus Preg Where the apparent reflectance is defined as TL E cosh p 10 105 where L is the recorded radiance signal E the extraterrestrial solar irradiance for the selected band and 0 is the solar zenith angle Following 25 the method can be described by the following set of equations TA PA 1 seA p r 10 106 Here pe is the reflectance of the cirrus cloud T the two way transmittance direct plus diffuse through the cloud p the reflectance of the virtual surface land or water surface including all effects of molecular and aerosol scattering below the cirrus and s is the cloud base reflectance of upward radiation Eq 10 106 can be simplified because of se p lt lt 1 yielding PA pe A TA A 10 107 With the assumption that the cirrus reflectance p A is linearly related to the cirrus reflectance at 1 38 um we obtain PA pel1 38um 10 108 where y is an empirical parameter derived from the scene scatterplot of p1 38 versus Preg land or p1 24 water It depends on the scene content cirrus cloud height and solar and viewing angles Fig 10 20 shows an example of such a scatterplot The red line is the left side boundary of data points that are not influenced by ground surface reflection i e cirrus contaminated pixels are clustered around this line and its slope represents
146. acting the path radiance Lp from the at sensor radiance L Ls x y L x y o L 2 Y z 10 26 CHAPTER 10 THEORETICAL BACKGROUND 195 In the ATCOR version of the IRC algorithm the path radiance varies spatially particularly due to the DEM height variation while a constant value per channel is used in the original IRC paper Then a regression analysis per channel of Ls versus cos is applied to calculate the slope m and intercept b After defining C m b the topographic correction map A is calculated coss C ho A z y 7 10 27 Y osale y C haa ho D Finally the surface reflectance p is computed according to T Ls x y 2z AY p x y LEE 10 28 La Y z Edir y z cos Biz Fas 2 Y 2 where T is the total ground to sensor transmittance and Edir Hai are the direct irradiance and diffuse solar flux on the ground respectively So the ATCOR version of IRC contains some improvements with respect to the original method the path radiance varies spatially mainly caused by terrain height variations possibly also due to visibility variations and the sky view factor can be provided from a ray tracing analysis instead of the local slope angle Note the IRC method usually performs well However due to the statistical evaluation of the regression analysis unphysically large gt 1 reflectance unit or small lt 0 surface reflectance values might happen for some pixels usually in areas with topographic shado
147. action of physical earth surface parameters such as spectral albedo directional reflectance quantities emissivity and temperature To achieve this goal the influence of the atmosphere solar illumination sensor viewing geometry and terrain information have to be taken into account Although a lot of information from airborne and satellite imagery can be extracted without radio metric correction the physical model based approach as implemented in ATCOR offers advantages especially when dealing with multitemporal data and when a comparison of different sensors is re quired In addition the full potential of imaging spectrometers can only be exploited with this approach Although physical models can be quite successful to eliminate atmospheric and topographic ef fects they inherently rely on an accurate spectral and radiometric sensor calibration and on the accuracy and appropriate spatial resolution of a digital elevation model DEM in rugged terrain In addition many surfaces have a bidirectional reflectance behavior i e the reflectance depends on the illumination and viewing geometry The usual assumption of an isotropic or Lambertian reflectance law is appropriate for small field of view FOV lt 30 scan angle lt 15 sensors if viewing does not take place in the solar principal plane However for large FOV sensors and or data recording close to the principal plane the anisotropic reflectance behavior of natural surfaces cause
148. ad Sensor Meta Data ENVI BIP Image Show System File ENYI BIL Image Edit Freferences ERDAS Imagine QUIT Landsat 8 OLI TIRS Landsat 8 OLI Hyperion Image TIF Hyperion Raw Image BSO Figure 5 2 The File Menu 5 1 1 Display ENVI File Use this function for displaying a band sequential ENVI formatted file in a simple way t AALMD N ENVI Band Selection 14 E Red Hr 14 a p Band 14 637 60000 15 4000 band 14 E N E reent peg la Band 7 530 70000 16 4000 band 7 2 o Nes Band 2 454 70000 13 6000 band 2 Single Band Default FGE Default CIF Cancel Select ts Figure 5 3 Band selection dialog for ENVI file display The ENVI format is a raw binary file accompanied by an ASCII header hdr in ATCOR it should be stored in band sequential order CHAPTER 5 DESCRIPTION OF MODULES ol An initial dialog allows to select the band s to display either a true color CIR color or a single band mode may be selected After display the following options are available within from the menu File Display ENVI Image Displays an additional ENVI image in a new window sorry no link ing available File Show ENVI Header Displays the ENVI header of the current image in a new editable window This allows to make changes to the ENVI header Note that the file needs to be loaded from scratch if changes have been made File Band Selection Allows to select a new combination of spect
149. al spectral region from 8 14 um the radiance signal can be written as L Lpath TEL BB T 7 1 e F 7 3 1 28 CHAPTER 3 BASIC CONCEPTS IN THE THERMAL REGION 29 O E Figure 3 2 Radiation components in the thermal region L Lp L T Lgg T L 7 1 e F r where Lpath is the thermal path radiance i e emitted and scattered radiance of different layers of the air volume between ground and sensor T is the atmospheric ground to sensor transmittance e is the surface emissivity ranging between 0 and 1 Lgp T is Planck s blackbody radiance of a surface at temperature T and F is the thermal downwelling flux of the atmosphere see Fig 3 2 So the total signal consists of path radiance emitted surface radiance and reflected atmospheric radiation The adjacency radiation i e scattered radiation from the neighborhood of a pixel can be neglected because the scattering efficiency decreases strongly with wavelength For most natural surfaces the emissivity in the 8 12 um spectral region ranges between 0 95 and 0 99 Therefore the reflected downwelling atmospheric flux contributes only a small fraction to the signal Neglecting this component for the simplified discussion of this chapter we can write E L Lpath _ 0 T ciDN Ligagh Lgr T 3 2 TE TE In the thermal region the aerosol type plays a negligible role because of the long wavelength and atmospheric water vapor is the dominating pa
150. ally by this routine If the ilu has already been calculated before it should be either removed or be smoothed separately A D A Xx Smooth a DEM Select Input DEM File Name src_idl atcor atcor_23 deno_data tm_rugged tn_blforest_ele bsg Dimensions 500 500 Diameter of DEM Filter Pixels E Define Output Name of Filtered DEM Ysrc_id atcor atcor_23 demo_data tm_rugged tm_blforest_sm3_ele bsq Help Smooth Median Done Figure 5 28 Panel of DEM smoothing 5 3 8 Quick Topographic no atm Correction The quick topographic correction routine TOPOCOR tries to eliminate the slope aspect topo graphic effects and neglects the atmospheric influence The program runs very fast and the output image contains the modified digital numbers Processing with TOPOCOR can be done with or without display of images The DEM slope and aspect files have to be specified on a separate panel that pops up after the input image has been specified The topographic correction implemented here multiplies the DN value with a factor f that depends on the local solar zenith angle P and a weighting coefficient w The wavelength depending weighting w is based on a typical value of the ratio of the direct to diffuse solar flux on the ground f 1 1 cosOs cosp w if A lt llum 5 1 f c0s0O 5 cosB if A gt 1 1um 5 2 CHAPTER 5 DESCRIPTION OF MODULES 19 Os is the solar zenith angle of the scene For A gt 1 lum the diffuse f
151. alues are taken from 30 i e a 0 994 b 0 687 c 0 737 The final step calculates the actual emissivities using the P spectrum and Emin Emin o 1 10 42 A n 10 42 e for thermal band imagery with at least 5 channels the ISAC In Scene Atmospheric Com pensation method is available A detailed description is given by Young et al 104 The method does not require ancillary meteorological data or atmospheric modeling It neglects the downwelling thermal flux and employs the equation L Lp 7 Lgp T Lp Lsurface 10 43 This approximation is justified for pixels with a high emissivity close to 1 i e blackbody pixels First the highest brightness temperature Th for each pixel in each channel is computed based on the at sensor radiance L converted into brightness temperature In the current implementation only channels in the spectral region 8 13 um are employed for the maximum brightness temperature search because the spectral regions A lt 8 um and CHAPTER 10 THEORETICAL BACKGROUND 200 A gt 13 um are strongly affected by atmospheric water vapor absorption Next a reference channel is defined where most pixels with maximum brightness temperature occur Only those blackbody pixels are retained which have the maximum brightness temperature in this reference channel most hits method For these selected blackbody pixels the scatterplot of measured at sensor radiance L versus blackbody radi
152. ame _wv bsq Columnar amount of water vapor calculated from image Format ENVI single band file 16 bit signed integer unit cm i e g cm2 1 000 i e scale factor 1 000 Illumination Name outputnamej ilu bsq Illumination file created during processing in case no external floating ilu has been pro vided Format Scaled illumination in byte format from 0 to 100 single band Haze cloud water Name outputname _out_hcw bsq Haze cloud and water mask Format ENVI byte image classes Fractional shadow Name outputname _fshd bsq Fractional cloud building shadow optional Format ENVI Integer image Scaled shadow between O and 1000 Diffuse irradiance Name outputname edif bsq Diffuse irradiance component 1 band 16 bit signed integer unit Wm nm Direct irradiance Name outputname _edir bsq Direct irradiance component 1 band 16 bit signed integer unit Wm nm Global irradiance Name outputname _eglo bsq Global irradiance sum of edir and edif 1 band 16 bit signed integer unit Wm nm Value Added Vegetation Name outputname _atm_fix bsq Multi Layer file containing side outputs for vegetation 1 e flux fapar savi etc Format ENVI signed 16 bit integer with scale factors as specified in the header 9 3 Preference parameters for ATCOR The preference parameters are now located in a user specific HOME directory idl rese atcor3 so multiple users of the same license
153. an 5 radiative transfer code This database has to be resampled for each user defined sensor Organization of the manual Chapters 2 and 3 contain a short description of the basic concepts of atmospheric correction which will be useful for newcomers Chapter 2 discusses the solar spectral region while chapter 3 treats the thermal region Chapter 4 presents the workflow in ATCOR and chapter 5 contains a detailed description of the graphical user interface panels of the major modules Chapter 6 describes the batch processing capabilities with ATCOR It is followed by chapters on value added products available with ATCOR sensor simulation miscellaneous topics and a comprehensive chapter on the theoretical background of atmospheric correction In the appendix the altitude profile of the standard atmospheres and a short intercomparison of the various solar reference functions is given Information on the IDL version of ATCOR can be found on the internet http www rese ch What is new in the 2015 version e A second haze removal algorithm is available It can be used as a pre processing step to the atmospheric correction It works on the original digital numbers of Level 1 products While the previous dehazing algorithm is embedded in ATCOR and performs haze removal and atmospheric correction the new algorithm is independent and can also be run without a subsequent atmospheric correction Additionally an atmospheric correction c
154. an be conducted after dehazing This de hazing can be run as batch or from a GUI e The cirrus removal is now also available without calculating the surface reflectance image So a Level 1 DN image with cirrus removal can be created see the list of batch job options in CHAPTER 1 INTRODUCTION 14 chapter 6 3 e The ozone influence in the 450 800 nm spectrum can optionally be included The standard LUTs are based on an ozone column of 330 DU sea level and if additional scene information on ozone is available from other sources it can be specified as an input parameter e The high resolution database is updated based on MODTRAN5 3 3 and HITRAN 2013 in stead of the previous HITRAN 2009 e The phase function LUTs phasefct bin have been updated and the sensor incidence angle on the earth surface replaces the sensor view or tilt angle new inn file e The thermal high resolution database is updated with a higher spectral sampling distance of SSD 0 4 cm for the wavelength region 7 10 jum i e corresponding to a wavelength SSD 2 4 nm and SSD 0 3 cm for the wavelength region 10 14 9 um SSD 3 5 5 nm instead of the former SSD 1 cm and SSD 0 5 cm e New sensors are supported Worldview 3 WV 3 CAVIS Gaofen 1 2 Naomi 1 Landsat8 OLI and upcoming Sentinel 2A e Spectral resampling of multiple spectra stored in separate ASCII dat files or multiple spectra in an ENVI spectral library fil
155. ance corresponding to Lgr Lies is computed for each channel This means the surface radiance of eq 10 43 is approximated as Lsurface LBB Lmar The final step is a least squares regression of the scatterplot data L versus Lsurface yielding the intercept path radiance Lp and slope transmittance T of eq 10 43 Care has to be taken to apply the regression only to the points near the top edge of all cluster points but allow some margin so the fitting line is allowed to sink by an amount of the sensor noise equivalent spectral radiance NESR The quality of the regression is significantly increased by allowing only those pixels in the scatterplot that had their maximum temperatures in the reference channel Two comments first because of the involved assumptions the obtained intercept is not the physical path radiance and the slope not the physical atmospheric transmittance Both quantities may be negative in some channels therefore they are referred to as unscaled path radiance p and unscaled transmittance 7 They might be rescaled to proper atmospheric path radiance and transmittance spectra e g using a radiative transfer code Second the ISAC method requires an adequate spread in surface temperatures in the scene and surface temperatures higher than the atmospheric radiation temperature So results for night time imagery will likely be degraded The compensated unscaled surface radiance spectrum is calculated as L
156. are counted as cloud although they might be something else e g snow or a specular reflection from a surface Only saturated pixels with a very high NDSI gt 0 7 are assigned to the snow class e medium cloud probability coded 60 same as for low probability but with p blue gt 0 25 and p red gt 0 18 10 66 This is similar to the standard cloud assignment in the _out_hcw bsq file where p blue gt Te Te 0 25 or 25 and p red gt 0 15 The 25 reflectance threshold in the blue or green band is the default value in the preference parameter file e high cloud probability coded 90 same as for medium probability but with p blue gt 0 35 and p red gt 0 25 10 67 If a thermal band exists the relationships 10 53 must also be fulfilled Water probability The criteria for the water class are described in the previous section The following water probability rules are employed e low water probability coded 30 water pixels fulfilling the above criteria eq s 10 48 10 49 This is the water assignment in the _out_hcw bsq file e medium water probability coded 60 same as for low probability but with a more stringent NIR reflectance threshold If no SWIR1 band around 1 6 um exists the criterion is o NIR lt 0 04 10 68 Note the default threshold Tivater Ir is 0 05 or 5 in the reflectance percent unit defined in the preference parameter file yielding more low probabi
157. arted and also for batch processing NOTE Preferences are set when one of the ATCOR modules has been opened So one should select one of the modules from within the menu ATCOR before editing the preferences CHAPTER 5 DESCRIPTION OF MODULES 58 000 Read Sensor Meta File and Create inn and cal Files V2 1 Pre defined Standard Sensors w DMC se RapidEye r Formosat sv SPOT 4 a KOMPSAT 3 MS w SPOT 5 we Landsatd S TH z SPOT 6 MS and Panchrom r Landsat 7 ETH z SPOT HS and Panchrom sw Landsat 8 we Theos se WordView 2 all 8 bands or 4 bands blue green red NIR or panchrom sv Pleiades sz blordView 3 VNIR SWIR or panchrom we QuickBird w E i User defined Sensors or Sensors with Water Wapor Channel s swe WordYiew 3 CAYIS se Hyperion we No meta file selected QUIT i Figure 5 9 Read sensor meta file OOO src_idl atcor atcor_23 cal ali ali_22dec20 File Help cl mW cm sr micron 3 4000 0 044 0 028 0 018 0 011 0 0091 0 0083 0 0028 0 00091 E al 4 E E T E 3 S il Pe Figure 5 10 Displaying a calibration file same file as in Fig 5 8 CHAPTER 5 DESCRIPTION OF MODULES 99 Figure 5 11 Panel to edit the ATCOR preferences CHAPTER 5 DESCRIPTION OF MODULES 60 5 2 Menu Sensor The menu Sensor is used to create and edit a new sensor from calibration information This function is only required if
158. ase of a 3 class emissivity file vegetation e 0 97 soil e 0 96 others e 0 98 is calculated on the fly Its output file name is 2mage1_atm_emi3 bsq the 3 indicating the 3 classes CHAPTER 4 WORKFLOW 39 In case of standard sensors with multiple thermal bands e g ASTER the spectral emissivity channels are not computed and ATCOR uses only one of the thermal bands band 13 in case of ASTER If ASTER band 13 is offered as a single channel input file to ATCOR the emissivity is set to a constant value of 0 98 for the surface brightness temperature calculation If all reflective bands and the thermal bands of ASTER are geocoded and offered as a 14 channel file to ATCOR then the 3 class emissivity option is also available If a user is interested in derived surface emissivity data the corresponding instrument e g ASTER has to be defined as a user specified sensor see chapter 4 6 4 6 User defined hyperspectral sensors Examples of satellite hyperspectral sensors are Hyperion and CHRIS Proba Since the channel center wavelength of these instruments may change from scene to scene they have to be treated as user specified sensors and a flexible interface has been implemented to enable the calculation of atmospheric LUTs adapted to the spectral channel properties This interface is similar to the one for the airborne ATCOR and the corresponding part of the airborne ATCOR user manual is repeated here The hyperspectral t
159. asses are assigned correctly and the emissivity value assigned to the channel with the max imum temperature is close to the actual channel emissivity Maximum surface emissivities usually lie in the 10 5 13 um region After calculating the surface temperature the emis sivities for all channels are computed e the ASTER TES algorithm is available for sensors with at least 5 channels in the thermal interval Az Ay with Ay FW H Mz 2 gt 8 08 um and Ay FWHM 2 lt 13 0 um to exclude channels in strong absorption regions The TES algorithm implemented here consists of 3 major parts Gillespie et al 1998 30 the NEM algorithm described above the ratio module It calculates relative emissivities 6 channel i by ratioing the NEM emissivity values e to their average bi i 1 n 10 39 2 Here n is the number of channels in the allowed thermal interval maximum minimum distance MMD module The maximum and minimum values of the relative emissivities 8 spectrum are calcu lated to find the spectral contrast MMD mazx 8 min 6B 10 40 Then an empirical relationship is used to predict the actual emissivities e from the MMD employing a regression with laboratory or field emissivity spectra Emin a b MMD 10 41 For small values MMD lt 0 03 e g water vegetation the value min is set to 0 983 The 3 parameters a b c of the regression can be specified by the user the default v
160. ata7 spectra field1 slb The resampled spectra are written to file data7 spectra field1_xxx slb e spect_rxz input input rc xc yc yc box box spnam spnam Extraction of a reflectance spectrum from a Level 1 image DN image file name input The spectrum is taken from the center coordinates xc yc and averaged over a square box of box box pixels The name of the spectrum is specified with the keyword spnam The corresponding inn file must exist and all parameters for the calculation of the surface re flectance are taken from this file If box is an even number it is replaced with the next higher odd number to uniquely define the box center If box is not specified then box 1 is taken NOTE The above spect_xx is a symbolic placeholder There are four implementations flat and rugged terrain satellite and airborne ATCOR spect_a2 satellite version flat terrain spect_a3 satellite version rugged terrain spect_4f airborne version flat terrain spect_4r airborne version rugged terrain CHAPTER 6 BATCH PROCESSING REFERENCE 135 So an example of invoking this feature is spect_4r input datal mission2 scene15 bsq xc 500 yc 600 box 3 spname target15_n1 Two ASCII output files will be created in the folder datal mission2 target15_n1 dat the surface reflectance spectrum 2 columns wavelength reflectance 7 target15_nl rdn 4 columns wavelength at sen
161. atabase while the new database includes 10 characters from the Ez file name e g atm_database_kurucz2005 The ATCOR tools panel contains the program to convert from one to another spectral irradi ance database see Figure 9 3 It enables an update of the monochromatic atmospheric database without the need to repeat the time comsuming MODTRAN 5 computations involving the cor related k algorithm in some spectral regions The user can also provide additional solar irradiance files to the sun_irradiance folder provided the spectral range increment and irradiance unit agree with the template spectra Attention ATCOR will always work with files in the active folder atm_database therefore the old atm_database has to be renamed or deleted and the folder with the new database has to CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 153 ATCOR folder file sun_irradiance e0_solar_fl e0 solar_f2 e0_solar_f3 atm_database e0_solar_ft atm_database f2 e0_solar_f2 Figure 9 2 Solar irradiance database be renamed as atm_database before applying the sensor specific resampling program RESLUT Since each atm_database folder contains its corresponding solar irradiance spectrum a unique identification is always possible Previously generated channel resampled atm files do not have a reference to their solar irradiance file but
162. ate A simple criterion vegetation index P850nm P660nm gt 3 is used to distinguish soil sand and vegetation The right part of Figure 10 27 shows the effect of shifting the threshold illumination angle 6r For larger values of Pr the decline of function G starts later with a larger gradient and the lower bound g is met at slightly higher values of 6 In most cases g 0 2 to 0 25 is adequate in extreme cases of overcorrection g 0 1 should be applied Practical considerations The angle r can actually be calculated from the imagery as demonstrated by the following ex ample When processing the scene with ATCOR the map of local solar zenith angles is stored in a separate file ilu If the output file after atmospheric topographic correction contains bright overcorrected areas this file should be linked to the ilu file using any available standard image processing software The ilu file contains the illumination map 5 scaled as byte data ilu 100 x cospi Bi arccos ilu 100 10 125 10 126 Let us assume an example CHAPTER 10 THEORETICAL BACKGROUND 234 A pixel in a dark area of the ilu image has the value ilu 32 i e 6 71 The overcorrected reflectance value be pz 80 and this value shall be reduced to 40 a value typical for the flat terrain neighborhood Then the threshold angle has to be specified such that cos cosGp 0 5 with exponent b 1 in equation 10 123 in this case Br 50
163. ate the heat fluxes derived from the vegetation and urban model respectively For the urban surfaces asphalt concrete the G veg H veg and LE veg values are given in brackets for comparison but the corresponding urban heat fluxes are valid because the urban criterion equations 7 23 peso gt 0 10 ps50 gt 0 10 and peso gt Paso 0 7 applies The last row repeats the concrete case for Rsolar 800 1 0 36 512 Rn Reotar Ratm Rsurface 512 100 412 Wm a realistic reduced R value compared to the asphalt where Eg 800 Esolar 800 1 0 12 700 R 700 100 600 Wm full veget partial veget i dark asphalt l E bright concrete l l bright concrete 0 7 Table 7 1 Heat fluxes for the vegetation and urban model All fluxes in Wm All radiation and heat fluxes are calculated in units of Wm They represent instantaneous flux values For applications where daily 24 h LE values are required the following equation can be used for unit conversion cm 1 jas I day 286 The latent heat flux LE is frequently called evapotranspiration ET Although LE and ET are used interchangeably the unit cm day or mm day is mostly employed for ET For water surfaces the distribution of net radiation into G LE and H is difficult to determine because it depends on several other parameters Therefore G and H are set to zero here and so LE equals Rp LE E
164. ative values 5 1 9 Read Sensor Meta Data This function reads standard Meta information as provided by the data providers together with the imagery The meta data is read and stored into the standard inn file and a cal file is created to be used for subsequent ATCOR processing The list of supported sensors can be seen in Figure 5 9 i e DMC Formosat Landsat Pleiades Rapideye SPOT Theos Quickbird and World View 2 3 ZY 3 and more 5 1 10 Show System File This is the same function as Show Textfile but defaults always to the ATCOR installation in order to allow to select an ATCOR system file from within the installation such as the cal files the solar reference files the sensor definition files The function then allows to adjust and save the respective text contents of the selected file CHAPTER 5 DESCRIPTION OF MODULES ov 000 Calibration File Plot File Fornt_Size Display Output Help fsro_id fateor fatcor2a ecal falifaliS2dec 0o4 cal Colibration Canstonts o0 Ctfset mh em ar ami X al Gan me er ar em GN 4 6 Band Number Wovalength Figure 5 8 Plotting a calibration file 5 1 11 Edit Preferences The default settings of ATCOR may be edited through this panel The updated preferences are then written to the ASCII file as displayed at the top of the panel The preferences persist for the user who started the ATCOR application the next time the system is st
165. atmsopheric conditions or to compare hyperspectral hs data with broad band multispectral ms data As a restriction the TOA top of atmosphere or at sensor radiance calculated with the TOARAD program assumes a nadir view The hs2ms hyperspectral to multispectral program requires the hs and ms center wavelengths and the ms channel filter curves for resampling In addition noise of the ms sensor can be included as Gaussian noise with a specified amplitude either as noise equivalent radiance NER or as noise equivalent reflectance NEAp The hs contribution to a certain ms band is weighted with the value of the ms response curve at the corresponding hs wavelength compare Fig 8 1 After summing all contributions the result is normalized with the sum of the hs filter values Y Lys Rins Ahs Lms 1 E 8 1 k 1 where L denotes at sensor or TOA radiance RO the ms response function of channel i and n is the number of hs channels covered by the i th ms filter function A similar equation is used for the resampling of surface reflectance The weight factors wz for each hs channel are calculated with 145 CHAPTER 8 SENSOR SIMULATION OF HYPER MULTISPECTRAL IMAGERY 146 Normalized Response Function 0 62 0 64 0 66 0 63 0 70 Wavelength zem Figure 8 1 Weight factors of hyperspectral bands The solid curve shows the response function R of a ms channel Ne and the dashed lines indicate the hs center wavelengths A
166. b_04hm exportedatacdatar atcore s atm_database_thi Number of files to be converted 24 File 1 of 24 File 10 of 24 File 20 of 24 File 24 of 24 TONE time 40 sec All bp files converted ont exportrdatar datar atcor2 3atm_database_thuzb0s_RSr Output directory also contains reference irradiance eQ_solar_thu2003_RSL_ku2005_O4nm dat QUIT Figure 9 3 User interface to convert database from one to another solar irradiance all the LUTs atm from the input atm_lib xxx are replaced with the resampled selected irra IIINE diance spectrum This new folder also contains a file irrad source txt identifying the selected irradiance source 9 2 Supported I O file types The input image to ATCOR must have the band sequential BSQ ENVI format or the TIFF format Some restrictions apply to the TIFF format as detailed below Several data types exist for the encoding The following data types of an input image are supported e byte or unsigned 8 bit integer ENVI data type 1 e signed 16 bit integer ENVI data type 2 e unsigned 16 bit integer ENVI data type 12 e signed 32 bit long integer ENVI data type 3 e float 32 bit ENVI data type 4 CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 155 Resampling from high spectral resolution database fexport data data atcor2 3 atm_database To sensor specific atmospheric library fexport data data atcor2 3 atm_lib chris_modeth
167. bands within the same detector or spectrometer unit Optionally the polynomial coefficients can be set to zero in atmospheric absorption regions to expedite the processing CHAPTER 5 DESCRIPTION OF MODULES 114 The same approach is used for FWHM detection with the difference that not the position of the spectral bands is varied but the FWHM of the spectral bands is scaled in a systematic way Once the coefficients have been determined they are converted into the required file format and are placed in the respective sensor folder for a subsequent fully automatic radiometric and atmospheric processing Fig 5 72 shows the panel of the smile detection module Inputs Input file calibrated ENVI image file The file should be statistically as uniform as possible in order to get valid averages Atmospheric Database File non resampled atmospheric database file most appropriate for the selected image take care of the flight altitude and aerosol model Sensor spectral response First band of sensor spectral response file s rsp The correspond ing pressure definition file pressure dat is selected automatically from the same directory as the rsp files if available Output smile coefficients file name of ASCII file which is written and contains the 4th order polynomial coefficients This file may be copied to the sensor definition as smile_poly_ord4 dat for use as smile definition with a sensor Detection resolution finest s
168. base is calculated for a certain irradiance E A and the corresponding file e0_solar_xxx dat is included in the directory atm_database Beginning with the ATCOR 2011 release there is an option to switch from one extraterrestrial solar irradiance source E A to another one E2 X The delivered high spectral resolution database of atmospheric LUTs is based on the Fontenla 2011 solar irradiance spectrum Fontenla et al 2009 2011 22 23 It represents the solar irradiance for a quiet or low activity sun and is recommended as the standard spectrum The original 0 1cm resolution spectrum is convolved with Gaussian filter functions FWHM 0 4 nm and mapped on an equidistant 0 4 nm grid The file name of this spectrum E A is e0_solar_fonten2011_04nm dat If Ri denotes the set of quantities path radiance direct diffuse solar flux based on E A then the new set R with the irradiance spectrum Ex A is calculated as RalA Ry A E2 A E1 A 9 1 Figure 9 2 presents a schematic sketch of this conversion The folder sun_irradiance contains a number of solar irradiance files that can be selected The folder of the atmospheric database DB pertaining to E A includes the corresponding irradiance file e g e0_solar_fontan2011_04nm dat and the calculated new database D B includes the Ex A file e g e0_solar_kurucz2005_04nm dat The standard or active database is named atm_d
169. beled as clear if p NIR lt Ti clear clear pixels 10 101 The default value is T clear 0 04 i e 4 The value is one of the editable preference parameters see chapter 9 3 Thin haze over water is defined as T clear lt p NIR lt 0 06 thin haze 10 102 Medium haze over water is defined as 0 06 lt p NIR lt To haze medium haze 10 103 The default value is T2 haze 0 12 i e 12 This value is also one of the editable preference parameters The third step is a linear regression between haze pixels in the NIR band and each other reflective band The regression is iterated with only those pixels deviating less than half a standard deviation from the average If a and 6 denote offset and slope of the regression line respectively the de hazed pixel for each channel j can be calculated as DN corrected j DN original j a 6 DNnir DN clear 7 10 104 where DN clear j is the average of all clear water pixels in channel j The same technique is also employed to remove sun glint The main problem is the specification of the clear water threshold If the threshold is too low clear water pixels are included in the haze mask if it is set too high haze or sun glint pixels will be included in the clear pixel class There is no unique solution because sandy bottoms over shallow water can have a similar spectral reflectance behavior as haze so the clear water threshold is scene depende
170. cal directional reflectance weighted with the global flux on the ground E is used as a substitute for the surface albedo bi hemispherical reflectance It is calculated as 2 5um J POE AJAA _ 0 3um 2 5um 7 6 J Eg A dr 0 3um Since most satellite sensors cover only part of the 0 3 2 5 um region the following assumptions are being made for extrapolation Extrapolation for the 0 30 0 40 um region e P0 3 0 4u m 0 8 p0 45 0 50um if blue a band 0 45 0 50 um exists e P0 3 0 4um 0 8 P0 52 0 58um green band no blue band available Extrapolation for the 0 40 0 45 um region e P0 4 0 45um 0 9 P0 45 0 50um if a blue band 0 45 0 50 um exists e P0 4 0 52um 0 9 P0 52 0 58um green band no blue band available The reflectance reduction factors in the blue part of the spectrum account for the decrease of surface reflection for most land covers soils vegetation The extrapolation to longer wavelengths is computed as e If a 1 6 um band exists CHAPTER 7 VALUE ADDED PRODUCTS 139 P2 0 2 5um 0 5 P1 6um if ps50 pe50 gt 3 vegetation P2 0 2 5um P1 6um else e If no bands at 1 6 wm and 2 2 um are available the contribution for these regions is estimated as P1 5 1 8um 0 50 po g5um if p850 p650 gt 3 vegetation P2 0 2 5um 0 25 Po 85um if pss50 pe50 gt 3 P1 5 1 8um P0 85um else P2 0 2 5um P0 85um else At least three bands in the green red and nea
171. can retain their personal preference settings The path of the last input image is saved separately for the flat and rugged terrain versions of ATCOR CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 159 i e Ppreference_atcor2_path txt and preference_atcor3_path txt In addition the file prefer ence_parameters dat contains a number of default parameters that can be adjusted to scene prop erties This file contains the parameters with a short description line 1 A choice to set the water vapor values for water pixels 1 average water vapor value of land pixels is assigned to water pixels Option available for 1wwv_model 1 and iwv_model 2 see section 9 4 2 line average of water vapor of land pixels is assigned to water pixels Option only available with 2wv_model 1 see the job control parameter section 9 4 line 2 A cloud reflectance threshold Te in the blue green region to define a cloud mask Pixels belong to the cloud mask if al p blue gt Te or a2 p green gt Te asterisk apparent reflectance Typical values for T range from 15 35 If the cloud reflectance threshold is too high clouds will be included in the haze mask This will reduce the performance of the haze removal algorithm line 3 A surface reflectance threshold pw1 for water in the NIR band Pixels belong to the water mask if p NIR lt py only NIR band available line 4 A surface reflectance threshold p 2 for water
172. ce conversion of QuickBird data Technical note RS_TN_radiometric_radiance_4002 http www digitalglobe com Digital Globe 1900 Pike Road Longmont CO 80501 USA 2003 50 Kriebel K T Measured spectral bidirectional reflection properties of four vegetated sur faces Applied Optics Vol 17 253 259 1978 51 Levy R C et al Algorithm for remote sensing of troposheric aerosol over dark targets from MODIS collections 005 and 051 Revision 2 Feb 2009 2009 52 Li Z L et al A new appooach for retrieving precipitable water from ATSR2 split window channel data over land area Int J Remote Sensing Vol 24 3163 3180 2003 53 Liang S Falla Adl H Kalluri S Jaja J Kaufman Y J and Townshend J R G An operational atmospheric correction algorithm for Landsat Thematic Mapper imagery over the land J Geophys Res Vol 102 D14 17 173 17 186 1997 54 Luo Y Surface bidirectional reflectance and albedo properties derived using a land cover based approach with Moderate Resolution Imaging Spectroradiometer observations J Geo phys Res vol 110 no 1 p D01106 2005 55 Maignan F Brj8ejon F M and Lacaze R Bidirectional reflectance of Earth targets S 9 4 evaluation of analytical models using a large set of spaceborne measurements with emphasis on the Hot Spot 56 Makarau A Richter R Miller R and Reinartz P Haze de
173. ch image pixel with respect to north Format single band ENVI file 16 bit integer or float data type Unit degrees Skyview factor file extension dem _sky bsq Skyview Factor Format single band ENVI file byte data type Unit percentage 0 100 Cast shadow mask extension dem angles _shd bsq Shadow flag 0 cast shadow 2 no shadow 1 border region for each image pixel Format single band ENVI file byte data type Illumination file extension image ilu bsq External Illumination file Format ENVI floating point data file external illumination files CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 156 are only read if they are in floating point format and if they are in the same size as the imagery Contents a value of zero is a complete cast shadow whereas a value of 1 is in full illumination corresponding to the definition of the cos P of the incidence angle where 6 Odeg is a 90 degree incidence direction Calibration file extension sensorj cal Calibration file containing wavelength cO and cl for each spectral band for conversion of image data to calibrated radiance L c0 cl DN units mW cm sr um Format 3 column ASCII one header row Response Function extension bandxxx rsp Spectral response function for one spectral band with wavelength reference and relative re sponse Format 2 column ASCII no header This file is only requested for user defined sensors not for the standard sensors
174. ch is recommended then all output files are written to the specified output directory and the reflectance output file name is the name of the input file with _atm bsq appended The optional keyword vis can be used to overwrite the visibility value in the inn file For a constant visibility per scene npref 0 in the inn file the input vis value is the start value that will be iterated as described in chapter 10 4 1 In case of a variable scene visibility npref 1 the vis parameter is ignored if the scene contains enough dark reference pixels If not the program switches to the constant visibility mode and vis is used as a start value A negative vis value means the value abs vis is used for processing even if it causes a large percentage of negative reflectance pixels The optional keyword t1ff2envi can be used if the input file has the TIFF format tiff2envi 1 produces an automatic conversion of the input TIFF file e g imagel tif into the corre sponding ENVI file e g imagel_envi bsq This file is used as a temporary input file to ATCOR and it will be automatically deleted when the ATCOR run is finished Two output surface reflectance files will be generated if tiff2envi 1 an ENVI file imagel_envi_atm bsq and a TIFF file imagel_atm tif The ENVI output is needed if the spectral classification SPECL module is run otherwise it may be deleted by the user The de
175. cients a b of equation Tbb 1 a b In Lbb Tbb 270 330K Lbb mll m2 sr micron 1 040989E 02 7 734179E 04 band 14 max error K 0 07 Values are written to file asterl4_hs bbfit path srce_idl atcor atcor_23 sensor aster14_hs DONE es gff Quit X Figure 5 17 Black body function calculation panel 5 2 5 RESLUT Resample Atm LUTS from Database The monochromatic database of atmospheric LUT s has to be resampled for the specific channel filter functions of each sensor Details are given in chapters 4 6 1 to 9 1 4 Figure 5 18 repeats the panels of the LUT resampling program RESLUT The resampling has to be done separately for the reflective and thermal region Only the required range of flight altitudes and aerosol types should be selected CHAPTER 5 DESCRIPTION OF MODULES 66 Figure 5 18 Panels of RESLUT for resampling the atmospheric LUTs CHAPTER 5 DESCRIPTION OF MODULES 67 5 3 Menu Topographic The Topographic menu contains programs for the calculation of slope aspect images from a dig ital elevation model the skyview factor and topographic shadow Furthermore it supports the smoothing of DEMs and its related layers Satellite AT C OR File Sensor Topographic ATCOR ERDF Filter Simulation Tools Help Licensed for Dani DEM Import P Global Elevation lt c DLR ReSe 2015 DEM Preparation basis Slope Aspect Arc GRID ASCII Skyview Factor Cast Shadow Mask
176. ck the file atcor sav It will be opened through IDL or the IDL virtual machine and the graphical user interface of Fig 4 1 will pop up Alternatively type atcor on the IDL command line after having added the atcor directory to the IDL search path A large number of processing modules is available from this level as described in chapter 5 Most of them can be used without reading a detailed manual description because they contain explanations in the panels themselves However the next section guides the ATCOR newcomer during the atmospheric correction of a sample scene 000 Satellite AT C OARA File Sensor Topographic ATCOR PRDF Filter Simulation Tools Help Licensed for Daniel Version 9 0 0 c DLRReSe 2015 Figure 4 1 Top level graphical interface of ATCOR The functions in the File menu allow the display of an image file the on screen display of calibration files sensor response curves etc see Fig 4 2 More details about this menu are given in chapter 5 1 The Sensor menu of Fig 4 1 is available if the module for hyperspectral or user defined sensors is licensed It contains routines to create spectral filter curves rectangular Gaussian etc from ENVI header or from an ASCII file provided by the user calculates atmospheric look up tables LUTs for new sensors and computes the radiance temperature functions for thermal bands see Fig 4 3 and chapter 5 2 The Topographic menu contains programs to impor
177. ction panel satellite version Model Options Roujean Geometric Kernel the Roujean Kernel is used for the geometric part of the model instead of the Li Sparse model Maignan Hot Spot Geometry use the improved hot spot geometry for the volumetric kernel as proposed by Maignan RSE 2003 Spectral Smoothing of Model Smooth the weighting functions in the spectral dimension this option is useful for hyperspectral instruments only to avoid spectral artifacts Model Interpolation Interpolate missing values in the model from neighbors by linear inter polation Default no interpolation Use Precalculated Model use an existing model file fitting to your data NOTE the model file should have the same number of bands and have the same calibration granularity as of the current settings Write ANIF outputs By default the corrected image is written Use this option to get the side outputs i e the files _anif anisotropy map and the bci which is the BRDF correction index used to discriminate the BRDF classes CHAPTER 5 DESCRIPTION OF MODULES 96 Calibration Granularity For calibration of the model the image is segmentated in a number of BRDF classes The number of pre defined classes can be selected calibration on a bigger number of classes leads to better accuracy of the correction while it requires good image statistics Reflectance scale factor Constant Factor which is to be applied to the input image which has
178. ctor 4 0 e 16 bit signed integer scale factor gt 10 0 typically 10 or 100 e float scale factor 1 0 e The corresponding negative scale factors except byte case provide an output reflectance cube allowing negative surface reflectance values only recommended in a test phase 9 2 3 Side outputs A number of side outputs is written by default after successful execution whereas some outputs are optional marked with an Log file Name outputname log Standard log file containing all necessary information about the data processing Format ASCII Error log file Name atcor_error log An error log file is written whenever an uncaught error occurred Please send this file to the software supplier in case of malfunction Format ASCII CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 158 Aerosol optical thickness Name outputname _atm_aot bsq Aerosol optical thickness map scale facor 1 000 Format 1 channel binary 16 bit signed integer DDV classification Name outputname _atm_ddv bsq Classification of dark dense vegetation cast shadows and water pixels map used for aerosol retrieval Format 1 band ENVI byte file containing the three classes Visibility index Name outputname _atm_visindex bsq Index of Visibility used for atmospheric correction if variable aerosol distribution has been selected as processing option Format 1 band ENVI byte file containing the indices Water vapor Name outputn
179. ctral polishing function applied to all image pixels If CHAPTER 5 DESCRIPTION OF MODULES 102 xxx_atm bsq is the atmospherically corrected input image then xxx_atm_polish bsq is the pol ished output reflectance cube and the spectral polishing function is stored in xxx_atm_polish dat an ASCII file with two columns containing the center wavelength of each channel and the polishing factor Figure 5 60 shows the GUI panel 5 6 5 Flat Field Polishing This routine is to remove spectral artifacts from atmospherically corrected imaging spectroscopy data Average residual gains and offsets are calculated by comparison of assumed flat field data values to the measured values for each spectral band to find a gain correction factor The flat field is searched automatically within an image as the spectrally least variable part of the image 600 X ATCOR Flat Field Polishing select Input File Name cubes hyper iorED1H1220642004125110PY_L1T_197band_subset bsq Type of Correction Function wv Gain and Offset Gain only Define Polished Dutput Data Cube cubes hyper ion 01H1220642004125110P _L1T_197band_subset_ff bsq Help Run Tone Figure 5 61 Flat field radiometric polishing Inputs Input file name usually output of atmospheric correction _atm bsq can be any kind of image Type of Correction Function e Gain and Offset calculate average residual gain and offset for each pixel and apply them as c
180. d It also includes situations with constant or spatially varying water vapor column contents 10 4 1 Constant visibility aerosol and atmospheric water vapor This is the easiest case for atmospheric correction Still it can often be applied if homogeneous atmospheric conditions exist These might be encountered for small area scenes i e high spatial resolution imagery If the sensor has no channels in atmospheric water vapor regions results of atmospheric correction are not sensitive with respect to the selected water vapor content and a climatological value e g midlatitude summer US standard or tropical water vapor profile is usually sufficient For hyperspectral instruments the processing has to include the image derived pixel by pixel water vapor map The program performs a check whether the specified visibility leads to negative reflectance pixels for dark surfaces in the red band 660 nm vegetation and NIR band 850 nm water If this is the case the visibility is iteratively increased up to VIS 120 km to reduce the percentage of negative reflectance pixels below 1 of the scene pixels During an interactive ATCOR session the user is notified and can continue with the recommended visibility update or with the initial visibility During batch mode operation the program continues with the updated visibility if the input visibility in the inn file is positive For a negative visibility in the inn file no visibility
181. d on the original non Gaussian filter curves The corresponding sensor with the new spectral calibration has to be added to the list of existing sensors see chapter 4 5 to process imagery CHAPTER 5 DESCRIPTION OF MODULES 117 Figure 5 713 SPECTRAL_CAL spectral calibration with the updated spectral calibration In case of non Gaussion filter curves the original channel response files band rsp should be copied to the new sensor directory applying the appropriate wavelength shifts For sensors with Gaussian filter curves the gauss_rsp module see chapter 5 can be applied to the sensor_new wvl file to generate the corresponding band rsp files Note that a change of the spectral calibration usually requires a radiometric re calibration 5 8 7 Calibration Coefficients with Regression This routine employs the rdn files obtained during the single target calibration the c1 option of ATCOR s calibration module to create a calibration file by rlinear regression Figure 5 74 CAL REGRESS radiometric calibration with more than one target So for n gt 2 the single target calibration is to be performed n times and the resulting rdn files radiance versus digital number are offered as input to the cal_regress program CHAPTER 5 DESCRIPTION OF MODULES 118 Inputs Number of calibration targets A maximum of 9 calibration targets may be selected The files rdn should hav
182. d on the given parameters and the given standard model No specific panel is displayed The routine asks for the input reflectance image All other infor mation is taken from the inn file Please make sure that the reflectance image spectral definition corresponds exactly to the chosen atmospheric library and sensor definition as of the inn file 5 7 2 TOA At Sensor Thermal Radiance This routine calculates an At Sensor Thermal Radiance Cube from an emissivity temperature cube where temperature is in the last band All parameters used for the processing are generated from the x inn file of the input cube If the function is called the cube is opened and the x inn file is read which results in an at sensor cube _toarad bsq 5 7 3 At Sensor Apparent Reflectance This routine calculates an at sensor apparent reflectance from a calibrated at sensor radiance image cube This routines alculates for each image band the following output Papp DN c1 co m d Eo cos O0 5 6 where DN stored data values c gain for conversion of DN to at sensor radiance Co offset for conversion to at sensor radiance d relative sun earth distance average d 1 Eo solar irradiance top of atmosphere NOT at aircraft altitude 07 solar zenith angle Inputs CHAPTER 5 DESCRIPTION OF MODULES 108 e input file name e calibration file name cal e solar radiation file e0_solar_ spc e output file name e scale fact
183. data J Geophys Research Vol 107 No D24 4774 4793 2002 1105 Zhang Y Guindon B and Cihlar J An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images Remote Sensing of Environment Vol 82 173 187 2002 Appendix A Altitude Profile of Standard Atmospheres This chapter contains the altitude profiles of ATCOR s standard atmospheres that are based on the MODTRAN code Only the lower 5 km altitudes are shown since this region has the largest influence on the radiative transfer results and usually comprises about 90 95 of the total water vapor column For multispectral sensors without water vapor bands e g Landsat TM or SPOT the selection of the atmosphere should be coupled to the season of the image data acquisition The influence of a large error in the water vapor estimate e g 50 on the reflectance retrieval is usually very small about 1 2 reflectance at a reflectance level of 40 for Landsat TM band 4 However for sensors with water vapor bands e g MOS B or hyperspectral sensors the water vapor content plays an important role For these sensors the database contains files with four water vapor columns 2 9 2 0 1 0 0 4 cm These are used to generate interpolated and extrapolated values for the LU T s In analogy to the files for the airborne version of ATCOR the file names are R99000_wv29_rura atm h99000_wv20_rura atm etc
184. desirable to have a DEM of a quarter of the sensor s spatial resolution or at least the resolution of the sensor s footprint which is seldom available 68 Even in the latter case errors in the matching of imagery and DEM can lead to large relative reflectance errors exceeding 100 for critical geometries principal plane e g a mountain ridge with half a pixel offset between imagery and DEM 68 Thus the quality of the required DEM will limit the final accuracy of the geo atmospheric image product in many cases For a flat terrain and larger off nadir view angles BRDF effects may have to be accounted for and the appropriate surface cover dependent BRDF model will influence the accuracy Thermal region In the thermal wavelength region beyond 8 um the surface temperature retrieval additionally depends on the correct choice of the surface emissivity In the ATCOR model the emissivity in one CHAPTER 10 THEORETICAL BACKGROUND 242 thermal band is based on a classification of the reflective bands if the sensor collects co registered reflective and thermal band data Depending on the surface cover classification vegetation soil sand asphalt water etc a typical emissivity value is assigned to each class 83 If the deviation of the true surface emissivity to the assumed emissivity is less than 0 02 a typical error margin then the temperatures will be accurate to about 1 1 5 K A rule of thumb is a surface temperature erro
185. diffuse radiation component as compared to wave lengths longer than 0 8 um The distinction of water bodies from cloud shadow areas may be difficult or impossible if it is based merely on spectral reflectance shape and amplitude information Water bodies should be excluded as far as possible to improve the performance of the de shadowing algorithm Currently water and cloud pixels are masked with the spectral criteria p 0 85um lt 5 and p 1 6um lt 1 water 10 119 p 0 48um gt 30 and p 1 6um gt 30 cloud 10 120 If no channel in the blue region is available a channel in the green 0 5 0 6 um or red part of the spectrum 0 6 0 68 um could be used as a substitute Both criteria do not uniquely define the corresponding class The water criteria allow some margin for turbid water in the NIR region The more restrictive criterion p 0 85 wm lt 3 would perform better for clear water bodies However it would fail for moderately turbid or muddy waters Other common water classification criteria such as average reflectance over all bands p lt 3 or p 0 4 0 6um lt 6 may also fail So one has to compromise and tolerate a certain amount of misclassification for a fully automatic algorithm CHAPTER 10 THEORETICAL BACKGROUND 228 The scaled shadow map 9 x y is written to an output file The histogram of the unscaled shadow function Fig 10 23 typically has a main peak at Par a smaller secondary peak at 3 due to
186. e commands will execute the tile processing A simple trick can be used to start the atcor_tile programs directly on the IDL command line without having to type atcor first just copy the atcor sav file to atcor2_tile sav and atcor3_tile sav The same can be done for atcor2_batch sav and atcor3_batch sav For the Linux Unix operation systems a symbolic link is sufficient e g ln s atcor sav atcor2_batch sav For Linux Unix users with a full IDL license a batch job can be started directly from the shell e g idl e atcor2_batch input export data data7 demo_data tm_essen bsq 6 3 Batch modules keyword driven modules Most of the modules are available in both modes interactive and batch If the atcor sav file is copied to atcor2_batch sav and atcor3_batch sav a batch job can be started immediately from the IDL command line otherwise atcor has to be typed first Hereafter a description of the batch modules and keyword driven modules is given In order to make the batch options available you first have to type atcor on the IDL command line then the ATCOR GUI selection panel pops up alternatively you may use the command restore atcor sav Disregard this panel and continue on the IDL command line with the name of the batch job module where all the input parameters have to be specified via key words Current batch programs are e slopasp_batch input filena
187. e e B10 e B11 0 95 H 0 5 km 1 z 3 4 Woter Yapar cm square Ts 228 K diamond Ts 288 K triangle Ts 298 K TloHset 2 K US Standard Atmosphere e B10 e B11 0 95 H 0 5 km 0 3 1 0 1 5 2 0 fad Woter Yapar cm Figure 9 8 Surface temperature error depending on water vapor column emissivity 0 95 CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 176 9 6 4 SPOT 1 to SPOT 5 The metadata is specified in two files a VOL_LIST PDF and a METADATA DIM The first file is intended for a quick overview the second file contains the complete set of specifications The absolute calibration gains for each band can be taken from either file and should be put into the corresponding cal file as they are In the METADATA DIM file the calibration gains are named PHYSICAL_GAIN The SPOT unit is 1 Wm sr um but it is automatically converted into the ATCOR radiance unit The standard offset values are zero Occasionally however for SPOT 4 5 data a slightly negative offset has to be introduced for band 4 1 6 um in cases when the scene water reflectance is too high it should be close to zero The geometry of data acquisition is described in the the METADATA DIM file The solar geometry is specified with the solar elevation and azimuth angle The sensor tilt geometry is defined by the incidence angle 0 at the earth s surface or the corresponding sensor tilt view angle 0y at the orbit altitude h see Fig 9 9 Both ang
188. e see end of chapter 6 3 e The image based smile analysis tool has been improved and enhanced in various ways more spectral features in the visible range have been added an option to search for the optimized FWHM has been added and overall accuracy has been improved by continuum removal based correlation e The import function for GEOTIFF and JPEG2000 variations have been updated and added e Extraction of a surface reflectance spectrum from a specified box in a Level 1 DN digital number scene The corresponding batch job performs the atmospheric correction based on the parameters in the inn file see end of chapter 6 3 e The new installation process allows for direct updates and components installation from within the software e A new batch call option of ATCOR has been added This allows to call ATCOR within a processing environment directly from the computer console e The resampling of atmospheric LUTs for a user specified sensor can also be submitted as a batch job by typing reslut_batch on the IDL command line see chapter 6 3 Chapter 2 Basic Concepts in the Solar Region Standard books on optical remote sensing contain an extensive presentation on sensors spectral signatures and atmospheric effects where the interested reader is referred to Slater 1980 94 Asrar 1989 4 Schowengert 1997 91 This chapter describes the basic concept of atmospheric correction Only a few simple equations 2 1 2 16 ar
189. e detection calculation Detect FWHM The FWHM variations are calculated instead of smile variations by the same technique Plot Smile Starts a plotting window to check the smile and lets you select suitable features for calibration Save Report Saves an informational report about the smile detection Outputs An ASCII file which may be used as smile or FWHM description file in the respective sensor directory Note that this file should be named smile_poly_ord4 dat or smile_poly_ord4_fwhm dat in order to be automatically recognized by ATCOR As a side output an IDL save dump x sav is written in parallel which contains all used param eters and the effectively calculated smile results in array smileresult 4 ncols 17 Here the first dimension 4 contains the center wavelength nm smile shift nm atmospheric transmittance and correlation coefficient The second dimension ncols refers to the number of image columns and the last dimension 17 contains results for the 17 feature wavelengths For example the cen ter wavelengths for all across track pixels and the 760 nm feature are stored in smileresult 0 5 because this feature is the third one and IDL arrays start with index 0 009090 X ATCOR Inflight Smile Detection Select Input File Name cubes apexzuerich_2014 M0070_ZPH_L_140414_a0l1w_calibr_cubed0l Select Atmospheric Database File Yerc_idl atcor atcor_4 atm_database h07000_wv20_rura bp
190. e emissivity Equation 10 47 is the ratio of covariance to variance accounting for the abbreviation SWCVR The two selected channels should be in the 10 5 12 5 um region where the emissivity of most land surfaces changes only slightly yielding an emissivity ratio e close to 1 yielding Rj 7 T Then the parameters a and b in eq 10 46 can be calculated from a regression of channel transmittances versus water vapor content using the pre calculated LUTs The moving averaging window box is selected at 100 m x 100 m The method requires moderate to high temperature contrasts in the moving window otherwise results are not reliable Therefore it is preferable to retrieve the water vapor map from channels in the solar reflective region if possible 10 2 Masks for haze cloud water snow A useful first step before executing an atmospheric correction is the calculation of a pixel map for haze cloud water snow etc Such a pre classification has a long history in atmospheric correction methods 24 42 64 65 66 43 53 It is also employed as part of NASA s automatic processing chain for MODIS 1 using the classes land water snow ice cloud shadow thin cirrus sun glint etc A similar approach is taken here The calculation is done on the fly and if the scene is named scene bsq then the corresponding map is named scene_out_hcw bsq There is also the possibility to provide this information from
191. e image pixel size specified on the main panel see figure 5 31 The physical units of pixel size m and adjacency range km are also used to calculate the equivalent number of pixels needed to cover the adjacency range 5 4 4 ATCOR2 User defined Sensors The menus for user_defined usually hyperspectral sensors share the same functionalities as de scribed above for multispectral systems in both flat and rugged terrain options The major dif ference is the requirement of a specific sensor definition ie the sensor definition is editable and adjustable The ATCOR2 variant is recommended to speed up processing time and for fast checks as hyperspectral image processing may be very time consuming in rugged terrain 5 4 5 ATCOR3 User defined Sensors This routine is to be taken if highest accuracy is required in terrain for imaging spectroscopy in struments The functionality is analoguous as described for the other panels CHAPTER 5 DESCRIPTION OF MODULES Figure 5 32 Panel for DEM files CANCEL CONTINUE Figure 5 33 Panel to make a decision in case of a DEM with steps 78 19 CHAPTER 5 DESCRIPTION OF MODULES 760 740 Elevation m 720 700 GO BO 100 20 40 Pixel Hurmnber Figure 5 34 Influence of DEM artifacts on the solar illumination image Top illumination with low pass filtered DEM middle illumination based on original DEM bottom 100 m transsect using original DEM pixel size is 6
192. e monochromatic atmospheric database e for the altitude 99 000 m that serves as flight altitude for space sensors see chapter 4 6 After running ATCOR for a certain scene and sensor a surface reflectance cube is obtained which is input to the TOA at sensor simulation that can be performed for a flat or a mountainous terrain A detailed description of the keywords of toarad follows CHAPTER 8 SENSOR SIMULATION OF HYPER MULTISPECTRAL IMAGERY 148 Figure 8 3 Graphical user interface of program HS2MS e input datal image_atm bsq the _atm bsq indicates a surface reflectance file which is the output of an ATCOR run The input file to ATCOR was datal image bsq and toarad extracts some information from the corresponding file datal image inn for example the sensor name The output file name is data 1 image_toarad bsq e atmfile h99000_wv29_rura this is an example of an atmospheric look up table file with a rural aerosol and a water vapor column of 2 9 gem see chapter 9 1 If the keyword atmfile is not specified then h99000_wv10_rura will be taken e elev 500 an example of a ground elevation at 500 m above sea level If elev is not specified then elev 0 is assumed However if the keyword elev is not specified and the datal image inn file contains file names for the DEM elevation slope and aspect then the DEM files are taken and the TOA calculation is performed for
193. e non linear radiometric range close to saturation This factor is only used for 8 and 16 bit signed or unsigned data not for float or 32 bit data Note on the cloud mask The criterion al or a2 is also coupled with the conditions p NIR p red lt 2 and p NIR p SWIR1 gt 1 and NDSI lt 0 7 where NDSI is the normalized difference snow index A quality or probability mask for clouds is generated with the above three conditions and different apparent reflectance thresholds T in the blue green spectral region For Te 15 we define a low probability cloud Te 25 a medium probability and Te 35 a high probability cloud The result is put into a cloud map file named image_quality bsq if the input scene is named image bsq The cloud mask in the image_out_hcw bsq file is based on the user defined threshold Te in the preference parameter file and the above three conditions Note on the water mask The NIR band surface reflectance threshold for a water mask is only used for sensors without visible bands For an instrument with visible bands better water masks are obtained with the criterion that the TOA reflectance p spectrum must have a negative gradient for bands in the visible to NIR dp A d However for a scene with a high average ground elevation we use gt 1 2 km above sea level the TOA reflectance criterion is again replaced with the NIR surface reflectance criterion because of the dist
194. e of Calculation cl c amp cl Humber of calibration targets 1 Target 1 box EB Ground reflectance Fi le woragesand ta dat Target 2 bax E ai ra lets Piiz i Results of calibration File datar atcor2 2deso_datar ta_flat sandl cal Bore lefinition of target center coordinates Click targets in zoom window e Specify xy coordinates Target 1 mouse button i left Target 2 1 mouse button 2 center Figure 5 36 Radiometric calibration target specification panel At the top line of the menu of figure 5 36 the mode of calibration is specified one or two targets can be employed by selecting the button cl or c0 amp cl respectively If a calibration is intended CHAPTER 5 DESCRIPTION OF MODULES 83 for n gt 2 targets each target has to be specified separately in the cl mode which creates a file target_irdn i 1 2 n with the name target_i specified by the user These files contain the radiance and corresponding digital number spectrum as a 2 column ASCII table and they are employed in the batch module cal_regress see chapter 5 to calculate a least squares regression for the calibration coefficients Next the target box size and the corresponding ground reflectance file have to be specified The button for the file name of target 2 is insensitive because the single target mode was selected here Then the file name for the calibration results should be specified T
195. e required to understand the key issues We start with the radiation components and the relationship between the at sensor radiance and the digital number or grey level of a pixel Then we are already able to draw some important conclusions about the radiometric calibration We continue with some remarks on how to select atmospheric parameters Next is a short discussion about the thermal spectral region The remaining sections present the topics of BRDF correction spectral radiometric calibration and de shadowing For a discussion of the haze removal method the reader is referred to chapter 10 5 3 Two often used parameters for the description of the atmosphere are visibility and optical thick ness Visibility and optical thickness The visibility horizontal meteorological range is approximately the maximum horizontal distance a human eye can recognize a dark object against a bright sky The exact definition is given by the Koschmieder equation o P 3 op gt oa where 8 is the extinction coefficient unit km at 550 nm The term 0 02 in this equation is an arbitrarily defined contrast threshold Another often used concept is the optical thickness of the atmosphere 9 which is the product of the extinction coefficient and the path length x e g from sea level to space in a vertical path D 2 2 The optical thickness is a pure number In most cases it is evaluated for the wavelength 550 nm Generally there is n
196. e start of tiled processing It is further described in section 4 2 below and chapter 5 4 The BR DF menu provides access to the BREFCOR BRDF effects correction method and to the nadir normalization for wide field of view imagery and to a mosaicking tool see chapters 5 5 and INIA The Filter menu provides spectral filtering of single spectra reflectance emissivity radiance provided as ASCII files spectral filtering of image cubes and spectral polishing see chapter 5 6 The Simulation menu provides programs for the simulation of at sensor radiance scenes based on surface reflectance or emissivity and temperature images see chapter 5 7 The Tools menu contains a collection of useful routines such as the calculation of the solar zenith and azimuth angles spectral classification adding of a synthetic blue channel for multispectral CHAPTER 4 WORKFLOW 32 Satellite ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Licensed for Dani DEM Import F Global Elevation c DLR ReSe 2015 DEM Preparation es Slope Aspect Arc GRID ASCII Skyview Factor Cast Shadow Mask Image Based Shadows TEM Smoothing Quick Topographic no Atm Correction Figure 4 4 Topographic modules sensors without a blue band e g SPOT spectral calibration conversion of the monochromatic atmospheric database from one to another solar irradiance spectrum and more see chapter 5 8 Finally t
197. e with two columns as input The first column is wavelength nm or um unit the second is reflectance or emissivity or something else e g spectral radiance The reflectance range can be 0 1 the intrinsic reflectance unit or the percent range 0 100 Figure 5 57 shows the graphical user interface The input spectrum is high spectral resolution data After specifying the first band of the sensor the resampling is performed for all spectral bands and the result is written to an output file again with two columns wavelength and resampled spectrum Pick Input Spectrum i Vexpart data data atcor2 3 spec_Lib Pull_resolution alfalfa dat Pick Response File first band of sensor Vexpart data data atcor2 3 sensor Chris_nodel_RE bando1 rsp Output Filename resampled spectrum t export data data atcor2 3 spec_ ib full_resolution alfalfa_Chris_mode1_RE dat RUN Resampling Status 1 0 A e A P Quit Figure 5 57 Resampling of a reflectance spectrum 5 6 2 Low pass filter a Spectrum This program serves to prepare target reference spectra for IFCALI inflight calibration Unwanted spectral noise is removed by lowpass filter i e spectral smoothing It requires an ASCII file with two columns as input The first column is wavelength nm or um unit the second is reflectance or emissivity or something else e g spectral radiance The reflectance range can be 0 1 the CHAPTER 5 DESCRIPTION OF MOD
198. ed on the fly requires more memory gt Check tm_blforest_ilu bsq for possible DEM related artifacts Message Cancel DK Figure 4 8 Panel for DEM files also contain default settings which can be used in most cases When the main panel Fig 4 6 is left and the SPECTRA or IMAGE PROCESSING sections are entered all information is written to a processing initialization inn file e g image inn When reloading the input file this information is read from the znn file so a new specification of all processing parameters is not necessary Therefore this nn file can also be used for a batch processing see chapter 6 The remaining sub chapters of chapter 4 may be skipped during the first reading if the definition of user defined hyperspectral sensors is not relevant These sub chapters can be consulted if specific questions arise e g about batch job processing 4 3 Survey of processing steps Figure 4 9 shows the typical workflow of atmospheric correction A detailed description of the corresponding graphicical user interface for each module is given in chapter 5 First the image is loaded with possibly some additional information DEM files Then the sensor has to be defined the radiometric calibration file and a basic atmosphere aerosol type combination e g a summer atmosphere with a rural aerosol It is recommended to check the validity of the calibration and to estimate the v
199. ed haze cloud water map is a useful optional output of ATCOR It is enabled by setting the parameter ihcw 1 in the preference_parameters dat file see chapter 9 3 If the file name of the imagery is mage bsq the corresponding map is named mage_ out_hcw bsq It is a 1 channel false color coded ENVI file In principle if a certain mask of mage_ out_hcw bsq say haze pixels contains artifacts it may be edited and if the edited file is named image hcw bsq it will automatically be used for the ATCOR processing This means ATCOR can repeat the processing with an improved edited haze mask The file mage hcw bsq can also be provided by an external ATCOR independent source In any case if this files exists ATCOR will skip its internal calculations of these masks and use the pre calculated map l l Edit File I Figure 4 15 Optional haze cloud water output file The haze cloud water file contains the following classes see Table 4 4 e land e water boundary layer haze two classes thin to medium haze and medium to thick haze e cirrus three classes for thin medium thick and cirrus cloud thick cirrus cloud provided a narrow channel around 1 38 um exists cloud over land cloud over water e snow requires a 1 6 um channel e saturated pixels using the criterion T gt 0 9 DN mar where T is a threshold set at 0 9 times the maximum digital number This criterion is only used for 8
200. ed to define a matched filter vector with three elements per pixel For each image pixel the surface reflectance in these three channels and the scene average reflectance of these channels are calculated to obtain the unscaled shadow function and finally the scaled shadow function The same shadow function is employed to de shadow the imagery not only in the initial three channels but for all channels of the sensor eq 10 118 Details of the method One of the most important parameters is the available number of spectral channels during the covariance matrix and matched filter part of the algorithm The minimum requirement is a band in the near infrared region 0 8 1 0 ym The performance usually increases significantly if two additional bands at 1 6 um and at 2 2 um are available i e a Landsat TM type of multispectral sensor Even for hyperspectral imagery these three bands around 0 85 1 6 2 2 um are sufficient for the matched filter calculation The usage of a hundred bands would not be helpful but only cause numerical problems during the inversion of the covariance matrix eq 10 114 Spectral channels from the visible region are merely employed for the masking of cloud regions not for the matched filter part because water vegetation dark soils and shadowed pixels all range within a few percent reflectance In addition the visible region is not very sensitive to partial shadow effects because of its larger fraction of
201. effective terrain reflectance p V x y p x y and p Vi x y p x y the diffuse flux is approximated as Eaif 0 y Edif flat b Tsun amp Y 2 cosB cos0 s 1 E b x y Tsunl2 Y 2 Veky x Y ag Edir flat L Y 2 Edif flat 2 Y 2 pilx y 1 Plx y 10 31 The first line describes the anisotropic and isotropic components of the diffuse flux the second line accounts for multiple terrain reflection effects Related quantities to the global spectral solar flux on the ground are the wavelength integrated global flux and the absorbed solar flux Wm These play a role in the surface energy balance and they are available as part of the value added channels see chapter 7 2 equations 7 9 7 10 Surface reflected radiance The ground reflected or ground leaving radiance per band can be obtained in addition to the spectral solar fluxes by setting the parameter irrad0 2 It is calculated corresponding to the surface reflectance cube p x y named scene_surfrad bsq For a flat terrain it is L surf x y Elglobal plx y 7 10 32 In case of a mountainous terrrain the direct and diffuse reflected radiation maps from the equations 10 30 and 10 31 are used L sur f dir x y Edir F Eqif p 2 y n 10 33 Again the same output file name is used scene_surfrad bsq 10 1 5 Thermal spectral region Similar to the solar region there are three radiation components thermal pa
202. efined ones CHAPTER 5 DESCRIPTION OF MODULES 111 PF green vegetation m B Ore l HE a is e Zas ah ne TA i sand bara sell E Reflectance 4 A A asphalt man made Reflectance 3 J S 1 0 1 5 g aa 1 0 1 5 2 0 Wavalength arm Wavalength mi Figure 5 69 Examples of reflectance spectra and associated classes Select Sensor y ALI w ASTER as DNC w IKONOS2 w IKONOS2_PAN w IRS AB w IRS1CD w IRSLCD_PAN w IRSPE_AWIFS w IRSP6_LISS3 w IRSP6_LISS4 lt LANDSAT4_5 w LANDSAT w LANDSAT _PAN w MERIS 27 MOS_B MSS w ORBYIEW w ORBYIEW_PAN y QUICKB w QUICKB_PAN w SAC_C_ZMMRS SPOT1_3 w SPOT1_3_PAN w SPOT4 w SPOTS SPOT5_PAN INPUT Reflectance IMAGE Vexport data data atcor2 3 deno_data tn_f lattm_essen1000_atm bsq OUTPUT Classification IMAGE Yexport data data7 atcor2 2 deno_data tn_f lattm_essen1000_atm_cla bsq CONTINUE Figure 5 70 SPECL spectral classification of reflectance cube 5 8 4 Add a Blue Spectral Channel This routine MAKEBLUE adds a blue spectral channel for multispectral imagery not containing a blue band The band is calculated from empirical relationships from the green red and NIR bands which ought to be present The routine simply asks for an input multispectral image to be processed NOTE the input image should have at least 3 bands and the first three bands are assumed to be the triple GREEN RED NIR CHAPTER 5 DESCRIPTION OF MODULES 112
203. els as defined by the NODATA keyword in the GRID file if output value is less than zero the mean of the read data is put to the missing pixels Actions View Parameters Shows the header information of the GRID file Read DEM Reads the DEM based on the given definition and stores it to a single channel ENVI formatted file if required Output converted DEM as ENVI file in arbitrary coordinates 000 x Read ARC GRID Digital Elevation Model ARE Input File Default output value for missing datat E Help View Parameters Read DEM Done Figure 5 21 Import DEM from ARC GRID ASCII 5 3 2 DEM Preparation The purpose of this program is to provide a tool to resize a DEM to an image cube s dimensions This menu function as depicted in Fig 5 22 allows to calculate both layers in one step CHAPTER 5 DESCRIPTION OF MODULES 69 600 Prepare and Resize a DEM Select Input DEM Name data humap vord 1 DEH den_30tc8n_ele bsq fArbitrary 1 1 62999750 244515 00 5 00000 500000 units Meters Select Input Image Name data hunap vord_dema hynap_geo bsq Arbitrary 1 600 630335 00 239275 00 5 00000 5 00000 1 units Meters Window Diameter for DEM Processor Pixels E Options F llrite Sloper spect Files F Write Skyview File E E Define Output Name of Filtered DEM Vdata hnap vord_1 DEH den_20to8n res_ele bsq The DEM haz been prepared Help Fur Done Fig
204. em console directly or from within IDL The latter provides a higher flexibility whereas the first is well suited for integration of ATCOR in a processing environment 6 1 Starting ATCOR from console ATCOR can be started directly from a console or from a different processing environment using the standard call to IDL run time environment This call will show a splash screen when starting IDL unless an IDL RT or a full IDL license is installed on the processing computer RT licenses can be acquired from exelisvis com at a significantly reduced price than full IDL licenses This makes this method useful for operational processing with ATCOR The call in a windows environment is Lidlpath idlrt exe rt atcorpath atcor_3 bin atcor sav args input RIF E output logfile Lelefile factor where idlpath is the path to the idl installation typically something like idl84 bin bin x86_64 On a unix macOSX system the call syntax is as follows Lidlpath idl rt bin atcor sav args input R F E output logfile lelefilel factor The idlpath in this case is something like exelis id184 bin Arguments input Input file to be processed or input reference file respectively R F E Flag for processing option R rugged terrain processing atcor3_batch F flat terrain processing atcor2_batch E elevation data preprocessing at_prepele 123 CHAPTER 6 BATCH PROCESSING REFERENCE 124 output Name of outp
205. emplate reference spectra from the spec_lib library The dashed spectra are resampled with the Landsat 5 TM filter curves file name of ATCOR will be imagel_atm bsq and a log report of the processing is available in ymagel_atm log Once an image is processed with ATCOR all input parameters are saved in a file inn that is automatically loaded should the image be processed again The recommended extension for the radiometric calibration files is cal Other extensions are ele bsq for the digital elevation file _slp bsq for the DEM slope file _asp bsq for the DEM as pect _sky bsq for the sky view file _ilu bsq for the solar illumination file in rugged terrain and _cla bsq for the classification map of SPECL The visibility index map is named _visindez bsq the aerosol optical thickness is _aot bsq the cloud building shadow map is named fshd bsq and the atmospheric water vapor map _wv bsq Thermal band imagery In case of thermal band imagery the surface temperature and emissivity are stored in separate files Surface temperature is appended to the reflectance cube i e is included as the last channel in the _atm bsq file e g imagel_atm bsq The surface temperature calculation is based on an assumption for the emissivity A constant emissivity or a surface cover dependent emissivity can be selected as options The simple c
206. en band is substituted and for the dense dark vegetation the surface reflectance relationship is used p green 1 3 p red 10 83 Now eq 10 79 is again employed for the green band instead of the blue band to calculate the path radiance the best match to a MODTRAN aerosol type and possibly a fine tuning of the path radiance Aerosol retrieval for VNIR sensors If no SWIR bands exist but at least a red band around 660 nm and a NIR band around 850 nm a different approach has to be taken see reference 73 for details It starts with the assumption of CHAPTER 10 THEORETICAL BACKGROUND 212 L Radiance L total blue band de reflected radiance blue band w ign a k w i e s my a wy w itn om ia blue green red Figure 10 13 Rescaling of the path radiance with the blue and red band After subtraction of the reflected radiance from the total radiance in the blue band the remaining signal is the updated path radiance in the blue band The path radiance of bands in the blue to red region is then rescaled with interpolation average clear atmospheric conditions visibility VIS 23 km to calculate the surface reflectance in the red and NIR bands which is appropriate for situations of clear atmospheres VIS 15 40 km The second step derives a mask of dark vegetation pixels using the ratio vegetation index rvi of the red and near infrared surface reflectance rvi Pnir Prea and multiple reflectance thresh
207. ent bands to be used for calculation on each side of the target band e g factor 3 uses 7 bands for polishing 3 on each side plus central band Smoothing Factor smoothing applied stand alone or in combination with the derivative filter 0 no smoothing 1 slight smoothing filter 1 4 1 2 moderate smoothing filter 1 2 1 3 standard smoothing filter 1 1 1 4 and more standard smoothing with moving average Polishing Filter Type Four options are available for statistical spectral polishing Derivative Filter all spectral bands of the given window size are taken into account to calculate deriva tive used to reconstruct the value of the center band Neighbour Derivative all spectral bands except for the center itself are taken into account to calculate derivative used to reconstruct the value of the center band Lowpass Filter Only the smoothing is performed no derivatives are calculated CHAPTER 5 DESCRIPTION OF MODULES 101 Savitzky Golay Filter to perform a numerical polinomial fit of 4th degree through the selected total window size Output A cube containing the spectrally filtered copy of the original image data cube is generated compare Paper Earsel SIG IS Workshop Edinburgh 2011 AOO A ATCOR Derivative Polishing Select Input File Name data hyper ion Bern_02 Hyper ion subl67 bsq Select Sensor Spectral Response src_idl atcor atcor_28 sensor hyper ion67 band001 rsp ELE Number of polishing band
208. ently The image scene and the meta file have to be stored in the same folder If the scene is named scene bsq or scene tif then the metafile inn has to be renamed into scene inn before starting the ATCOR run Reading of the meta file is invoked with the following command where ext is the extension of the meta file name e g xt xml IMD read_meta_dmc input folder metafile ext DMC read_meta_kompsat3 input folder metafile ext Kompsat 3 read_meta_landsat_tm input folder metafile ext Landsat 4 5 TM read_meta_landsat_etm input folder metafile ext Landsat 7 ETM Note calibration values are taken for the thermal band 61 low gain If band 62 high gain will be used then the parameters c0 62 c1 62 should be appended after the input file specification These will contain the corresponding offset and gain values to be updated in the cal file read_meta_landsat_oli input folder metafile ext Landsat 8 OLI CHAPTER 6 BATCH PROCESSING REFERENCE 136 read_meta_landsat_oli_pan input folder metafile ext Landsat 8 OLI panchromatic read_meta_pleiades input folder metafile ext Pleiades read_meta_rapideye input folder metafile ext RapidEye read_meta_spot5 input folder metafile ext 4 SPOT 4 read_meta_spot5 input folder metafile ext 5 SPOT 5 read_meta_spot6 inpu
209. epresenting average conditions However if ozone information is available from other sources and if it deviates more than 50 DU from the reference level 331 DU then it can be specified as an additional input parameter 81 The three most important atmospheric parameters that vary in space and time are the aerosol type the visibility or optical thickness and the water vapor We will mainly work with the term visibility or meteorological range because the radiative transfer calculations were performed with the Modtran 5 code Berk et al 1998 2008 and visibility is an intuitive input parameter in MODTRANO although the aerosol optical thickness can be used as well ATCOR employs a database of LUTs calculated with Modtran 5 Aerosol type The aerosol type includes the absorption and scattering properties of the particles and the wave length dependence of the optical properties ATCOR supports four basic aerosol types rural urban maritime and desert The aerosol type can be calculated from the image data provided that the scene contains vegetated areas Alternatively the user can make a decision usually based on the geographic location As an example in areas close to the sea the maritime aerosol would be a logical choice if the wind was coming from the sea If the wind direction was toward the sea and the air mass is of continental origin the rural urban or desert aerosol would make sense depending on the geographical locati
210. er to smooth spectral noise features requires at least 10 thermal bands image_atm_emiss_lp5 bsq is the same emissivity map but filtered with a 5 channel low pas filter to smooth spectral noise features requires at least 30 thermal bands image_atm_isac_emiss bsq emissivity cube for the ISAC algorithm image_at_sensor_channel_tmax bsq map of channel numbers with maximum at sensor tem perature image_at_surface_channel_tmax bsq map of channel numbers with maximum surface tem perature amage_at_sensor_tbb bsq at sensor brightness temperature cube image_at_surface_tbb bsq at surface brightness temperature cube The last channel of mage_atm bsq contains the surface temperature map evaluated with the ap propriate emissivity the preceding thermal channels in this file contain the surface radiance In case of the ISAC algorithm an additional file image_isac_lpath_trans dat contains the spectral path radiance and transmittance estimates for the scene Fig 4 16 shows an example of these spectra derived from a SEBASS scene unscaled Sc path radiance unscaled SA Tromamittance transmittance E E 3 10 11 12 13 14 F 8 3 10 11 12 13 14 Wavelength m Wavelength pam Figure 4 16 Path radiance and transmittace of a SEBASS scene derived from the ISAC method Fig 4 17 presents the at sensor at surface radiance and brightness temperatures The at sensor products clearly show the atmospheric absorption features
211. errain code Therefore the coding is done in separate modules as discussed below 10 7 1 Algorithm for flat terrain The complete sequence of processing for sensors with water vapor bands and a short wave IR band 1 6 or 2 2 um region consists of the following steps e masking of haze cloud water and clear pixels CHAPTER 10 THEORETICAL BACKGROUND 239 Figure 10 30 BREFCOR mosaic correction Top uncorrected Bottom corrected RapidEye chessboard image mosaic c DLR haze removal or cirrus removal de shadowing masking of reference pixels calculation of visibility visibility index and aerosol optical thickness for reference pixels For an efficient faster processing the float visibility range 5 190 km is converted into a discrete integer visibility index vi ranging from O 182 where the vi increment 1 corresponds to an aerosol optical thickness increment at 550 nm of 0 002 The lowest vi 0 corresponds to visibility 190 km and vi 182 to visibility 5 km The visibility visibility index of the non reference pixels can be defined as the average of the reference pixels or or a spatial triangular interpolation can be employed to fill the gaps Then a moving low pass window with a box size of 3 km x3 km or the minimum of ncols 2 and nlines 2 ncols image columns CHAPTER 10 THEORETICAL BACKGROUND 240 nlines lines is applied to smooth sensor noise and small scale variations of the spectral correlation
212. ers required for ATCOR The lower part of the panel contains buttons for selecting SPECTRA determining the aerosol type employing inflight radio metric CALIBRATION and starting the image processing The processing options are shown in the separate panel as described in section 5 4 11 The trivial panels e g band selection spatial subimage etc will not be shown here The panels should be filled or clicked in the top down direction The message widget at the bottom will display hints warnings or errors 5 4 2 ATCOR2 multispectral sensors flat terrain The panel as decribed above and in figure 5 31 will appear when ATCOR2 is selected 5 4 3 ATCOR3 multispectral sensors rugged terrain In case of the rugged terrain version of ATCOR the panel for the DEM files has to be specified in addition Figure 5 32 It pops up after the input file has been specified A quick quality check is performed on the DEM files The solar illumination file is calculated and if its standard deviation is large the panel of Figure 5 33 pops up with a warning In this case the DEM elevation file and the derived files of DEM slope aspect etc probably have a lot of large steps The DEM resolution is often not appropriate for high spatial resolution imagery and integer coded DEM s might have to be resampled and stored as float data Appropiate action for resampling or low pass filtering is recommended in these cases see the tips in chapter 9 5 Figure 5 34 s
213. eshold in the atcor preferences preference_parameters dat file The default threshold is 25 re flectance in the blue green spectrum T he opposite case can also happen if the cloud mask comprises too many pixels the threshold has to be raised e The haze mask is not appropiate This problem may be connected with the setting of the cloud threshold If the cloud threshold is too high cloud pixels are counted as haze and the results of the haze removal are bad because the haze algorithm is applied to clouds e The water mask is not appropiate This might lead to problems for the haze removal as well as the cloud building shadow removal i e water is erroneously counted as land and included in the land mask Enable the output of a haze cloud water map compare Fig 4 15 to check the water mask Read chapter 4 8 There are two possibilities to define the water mask If the average ground elevation of the scene is below 1 2 km the default option for cal culating the water mask is the negative gradient criterion for the apparent reflectance in the VNIR region see eq 9 3 This yields the best water map in most cases However in some cases urban areas with shadows cast by buildings the NIR SWIR1 water surface reflectance thresholds see chapter 9 3 yield better results If the average scene eleva tion is below 1 2 km one has to define negative reflectance values to both NIR SWIR1 thresholds to overrule the default negative
214. ess from sun photometer water vapor content from sun photometer or radiosonde and ground re flectance measurements to calculate the calibration coefficients cy c of equation 2 7 for each band For details the interested reader is referred to the literature Slater et al 1987 96 Santer et al 1992 Richter 1997 Depending of the number of ground targets we distinguish three cases a single target two targets and more than two targets Calibration with a single target In the simplest case when the offset is zero cy 0 a single target is sufficient to determine the calibration coefficient cy Li ci DN Lipath a TpiEg T 2 12 CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 22 Lpath T and Ey are taken from the appropriate LUT s of the atmospheric database p is the measured ground reflectance of target 1 and the channel or band index is omitted for brevity DNf is the digital number of the target averaged over the target area and already corrected for the adjacency effect Solving for c yields _ Ly Lipath i3 Tp Eg T DN DN C1 2 13 Remark a bright target should be used here because for a dark target any error in the ground reflectance data will have a large impact on the accuracy of c1 Calibration with two targets In case of two targets a bright and a dark one should be selected to get a reliable calibration Using the indices 1 and 2 for the two targets we have to solve the equations
215. et1_dn1 dat the DN spectrum The sequence of targetx_dnx dat files is used in the spectral calibration module 5 4 7 Aerosol Type The aerosol type is a parameter which is fixed for atmospheric correction This routine searches automatically for the best suited aerosol type for the currently selected image This type can then be used when selecting the atmospheric file 5 4 8 Visibility Estimate ATCOR uses the Dark Dense Vegetation DDV approach to calculate the best visibility for an image This button allows to calculate the visibility estimate for the whole image without going into the image processing Note that for the Variable Visibility option the visibility map will be calculated anyway from the image during the image processing 5 4 9 Inflight radiometric calibration module This chapter presents the GUI for the inflight calibration which may also be invoked from one of the four possible ATCOR main panels The purpose is the calculation of the radiometric calibration coefficients for spectral bands in the solar region based on measured ground reflectance spectra The user should be familiar with chapter 2 3 before using this function Note a ground reflectance spectrum from a field spectrometer has to be resampled with the channel filter curves of the selected sensor The field spectrometer file format should be converted into a simple ASCII file containing the wavelength nm or um in the first c
216. f column pixels of the image If the image is geocoded an interpolation from the 3 grid to the 1 grid is performed no hot spot case e The third step is a filter with a moving average window applied to the f function The following cases are distinguished if the image is not geocoded the window is 9 pixels without hot spot and 3 pixels with hot spot option If the image is geocoded the moving window extends over a 5 angular interval no hot spot and over a 3 interval with hot spot option Figure 10 26 shows part of a HyMap image acquired 3 June 1999 Barrax Spain 12 09 UTC containing the hot spot geometry The solar azimuth was 181 and the sensor scan line azimuth was 179 almost exactly pointing into the solar azimuth The left image shows HyMap band 30 at 868 nm after atmospheric correction The right image is the result after nadir normalization with a 1 sampling interval In this example the column means were calculated globally i e surface cover independent The algorithm also contains an option to compute the column means separately for 4 surface covers It can currently only be selected if the input imagery is reflectance data and not geocoded The processing time is much larger than for the global cover independent method The four surface classes are e bright vegetation ratio vegetation index NIR RED gt 10 CHAPTER 10 THEORETICAL BACKGROUND 231 e medium dark vegetation 6 lt ratio vegetatio
217. fault for a TIFF input file is t1ff2envi 0 i e the ENVI file conversion is switched off and no intermediate files are created T his will save disk space and some file conversion time However if the imagel ini file specifies the de hazing or de shadowing option then tiff2envi is reset to 1 to enable the creation of of the needed additional intermediate ENVI files For non TIFF files the tiff2envi keyword is ignored If each input band is in a separate TIFF file the files should be labeled consecutively e g image band1 tif image _band2 tif etc The output of ATCOR will be the corresponding surface reflectance files image_band1l_atm tif image _band2_atm tif etc A keyword iff2envi can be specified as a parameter of the batch job If not specified the default is t2ff2envi 0 which means no intermediate disk files will be created only the final atm tif With the keyword tiff2envi 1 the temporary file image_envi bsq is automat ically created and it contains the input data in the ENVI bsq band sequential format The CHAPTER 6 BATCH PROCESSING REFERENCE 128 standard output name of the atmospheric correction is image_envi_atm bsq and of course image_atm tif The image_envi bsq is deleted after processing but image_envi_atm bsq is kept because it is required for a run of the spectral classification SPECL module The user has to delete this file manua
218. flectance Thin cirrus over land is calculated with 1 0 lt p cirrus lt 1 5 10 58 employing the percent reflectance unit Medium thickness cirrus is calculated as 1 5 gt p cirrus lt 2 5 10 59 and the thick cirrus class consists of pixels with 2 5 gt p cirrus lt 4 0 10 60 The same definition is used for cirrus over water if the land water distinction is still possible based on the selected spectral criteria Still higher apparent reflectance values are defined as cirrus cloud if 4 0 gt p cirrus lt 5 0 10 61 and thick cirrus cloud if p cirrus gt 5 0 10 62 and no distinction concerning land water is made for the last two classes Cirrus detection is switched off in the following cases e no water vapor map available if DEM height 2000 m CHAPTER 10 THEORETICAL BACKGROUND 205 e water vapor map W available if W 1 cm or W Twv where Twv is the water vapor threshold specified in the file preference_parameters dat Haze over land see chapter 10 5 3 The the mean of the tasseled cap transformation TC is calculated Clear pixels are those with TC lt mean T C and p blue lt Te cloud over land threshold and p NIR gt Twater NIR wa ter reflectance threshold defined in preference_parameters dat Next the mean and standard deviation o of the HOT transformation are calculated Pixels are assigned to the compact haze mask if HOT gt mean HOT
219. flectance empirical BRDF correction depending on illumination map if enabled Le E adjacency correction including proper treatment of cloud areas 4 spherical albedo correction e The retrieval of surface temperature and emissivity includes the maps of visibility index water vapor if water vapor bands exist elevation and scan angle No slope aspect correction is performed in the thermal region 10 8 Accuracy of the method There is not a single figure that can be given to summarize the accuracy for all situations because the radiometric accuracy of the method depends on several factors the calibration accuracy of the sensor the quality of geometric co registration of the spectral bands the algorithm for ortho rectification relying on auxiliary information such as attitude and GPS DGPS the accuracy of the radiative transfer code MODTRAN 5 the correct choice of atmospheric input parameters the terrain type flat or rugged and the surface cover Solar region In the solar region wavelength lt 2 5 um assuming a flat terrain and avoiding the specular and backscattering regions an accuracy of the retrieved surface reflectance of 2 for reflectance lt 10 and 4 reflectance units for reflectance gt 40 can be achieved 71 For rugged terrain the most important parameter is an adequate spatial resolution of the DEM or DSM digital surface model and the exact ortho rectification of the imagery It would be
220. g correction e ibrdf 21 correction with sqrt cos of local solar zenith angle eq 10 123 with b 1 2 for soil sand Vegetation eq 10 123 with exponent b 3 4 and b 1 3 for A lt 720 nm and A gt 720 nm respectively i e option a in the BRDF panel see Figure 5 45 weak correction This is the recommended standard yielding good results in most cases e ibrdf 22 correction with sqrt cos of local solar zenith angle eq 10 123 with b 1 2 for soil sand Vegetation eq 10 123 with exponent b 3 4 and b 1 for A lt 720 nm and A gt 720 nm respectively i e option b in the BRDF panel see Figure 5 45 strong correction e beta_thr threshold local solar illumination angle Pr where BRDF correction starts If beta_thr 0 and brdf gt 0 then the angle r is calculated in ATCOR depending on the solar zenith angle and its value can be found in the corresponding _atm log file e thr_g g lower boundary of BRDF correction factor see chapter 10 6 2 eq 10 123 10 124 line 23 1 0 820 0 780 0 600 lai_model a0_vi al_vi a2_vi Parameters for the LAI model to be used if ksolflux gt 0 see chapter 7 line 24 0 900 0 950 0 380 c_fpar a_fpar b_fpar parameters for the fpar model to be used if ksolflux gt 0 see chapter 7 line 25 CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 166 20 0 0 83 air temperature C air emissivity see chapter 7 Parameters for the net flu
221. g 10 23 A threshold Py can be set in the vicinity of the local histogram minimum and the core shadow mask is defined by those pixels with P x y lt Py The details of the choice of Py are discussed below As always with thresholding some arbitrariness is involved in the final selection Once the core shadow mask has been defined it is expanded to include the surrounding shadow clear transition zone of 100 m width De shadowing with the scaled shadow function is then exclusively applied to the pixels in this final mask This means the direct solar flux Eg term in CHAPTER 10 THEORETICAL BACKGROUND 221 eq 10 112 has to be multiplied with 2 y m d ceo t a i DN x y Lp TiEdirid x y Faiz at In equations 10 112 10 118 the aerosol optical thickness or visibility required for the atmospheric terms path radiance transmittance direct and diffuse flux can be derived from the image pro pilu y 10 118 vided the necessary bands in the visible and shortwave infrared region exist and the scene contains dark reference areas 43 Otherwise the user has to specify an estimated visibility The second important atmospheric parameter is the water vapour column For instruments with bands in the atmospheric water vapour regions this information can be derived from the image data 87 otherwise an estimate has to be provided by the user In summary three channels around 0 85 1 6 and 2 2 wm are us
222. g with BCI so NDVI Cforest Csoils It takes into account the relatively higher reflectance of water in the green spectral band in relation to the blue for discrimination to other surface targets such as shadows and dark asphalt Coia 652 0 8 gt o 3 BOr 0 5 lt 0 10 135 Pblue The range of the final BCI function is defined between values of 1 20 and 1 50 The BCI index calculated in each image pixel can then be used for BRDF model calibration and subsequently for image correction Model calibration HDRF images RTLS model calibration set option Maignan BCI calculation RTLS kernels f_vol variation BCI l 1 103 f_geo variation A 0 25 to 0 75 level analysis fit polynomial 5 deg image based HDRF combinations in HDRF LUT functions for each best fit of absolute class cross track deviation from mean RMS f_vol f_geo f_iso for each band BCI level exclude outliers bad fits and incomplete levels level averages over all scenes parameter set for correction Figure 10 28 BRDF model calibration scheme For the calibration of the model the BCI is divided into a number of 4 6 discrete classes The evaluation has shown that increasing the number of classes often leads to worse fitting results and less stable BRDF correction whereas keeping the number of classes small is more stable A second CHAPTER 10 THEORETICAL BACKGROUND 237 outcome wa
223. gradient criterion In the latter case an in crease decrease of the absolute value of the thresholds will increase decrease the number CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 169 of water pixels If the average scene elevation is above 1 2 km the gradient criterion cannot be applied and the NIR SWIR1 water surface reflectance thresholds apply as defined in the pref erence parameter file chapter 9 3 These surface reflectance thresholds can be defined as positive values as the gradient criterion in not valid anyway or as negative An increase decrease of the thresholds will increase decrease the number of water pixels e Rugged terrain the slope and illumination maps show strong horizontal and vertical stripes Strong artifacts in the DEM files will immediately be visible in the atmospherically topo graphically corrected surface reflectance image This problem frequently occurs for resampled DEMs e g the original DEM resolution is 30 m which is resampled to a 5 m pixel size Ar tifacts will be enhanced especially if the stepsize of the original DEM height resolution is coded as integer Float data would have smoother transitions A simple way to get better results is the use of a larger kernel size for the slope aspect calculation e g kernel 5 or kernel 7 instead of the default kernel 3 pixels but this approach causes a reduction of the high frequency spatial information Attenti
224. gram of PhiU containing the threshold for core areas point 1 of Fig 5 39 the range of re scaling of PhiU point 2 and the current value for PhiS point 3 Now these three parameters can be modified and the results will again be shown as the corresponding quicklook image and histogram When good results have been obtained the parameters can be saved and the final image processing can take place Figure 5 40 presents an example with two iterations Results for iteration 1 contain too many shadow pixels black areas in the central section of the image therefore the threshold 0 15 was decreased to 0 38 parameter 1 of Fig 5 39 After de shadowing most areas in the shadow mask were overcorrected in iteration 1 therefore the maximum range 0 40 was decreased to 0 19 parameter 2 of Fig 5 39 see diagonal line from lower left to upper right in histogram of Fig 5 40 The shadow mask for iteration 2 is appropriate and no overcorrection effects can be observed CHAPTER 5 DESCRIPTION OF MODULES 3 Figure 5 39 Panel to define the parameters for interactive de shadowing Note when leaving the panel of Fig 5 39 it is possible to edit the cloud shadow map before continuing the scene processing using any available image processing software Then the edited map is employed for the processing T his provides some flexibility because it is difficult to calculate a satisfactory shadow map in all cases CHAPTER 5 DESCRIPTION OF
225. gth Three options exist for the reference wavelength grid 1 use wavelength corresponding to the center of the detector array 2 use average wavelength over all detector columns per band 3 use nominal wavelength specified in the ENVI header of the reflectance cube The new reference center wavelengths are included in the header of the output file If the input filename is path1 image_atm bsq the output name is path1 image_atm_smcorr bsq indicating the smile corrected common wavelength grid Function parameters are filename is the full name of the surface reflectance file fpname is the full name of smile_poly_ord dat i e including the path number is the above option number 1 3 and if the keyword silent is set the progress about the band processing is not issued to the command line This module is also available in the interactive mode see main menu Filter Spectral Smile Interpolation Image Cube chapter 5 6 e S_oli_envi tifname Here tifname is the complete name of the first band of the Landat 8 TIF files including the folder Example 18_oli_envi datal scene_B1 TIF Then a layer stacked ENVI bsq file for the OLI bands is created 8 channels wavelength ascending without panchromatic named datal scene_OLL bsq e landsat amp _envi tifname Here tifname is the complete name of the first band of the Landat 8
226. h respect to north see Fig 9 10 then the satellite azimuth angle dy as viewed from the scene center is e dy a 270 if tilt incidence angle is positive L left case east e dy a 90 if tilt incidence angle is negative R right case west N view azimuth g 90 orientation angle i i a descending solar A azimuth 5 Pi node P ae ss view azimuth 270 Figure 9 10 Solar and view geometry Attention e Tricky The orientation angle is specified in the VOL_LIST PDF but does not show if the METADATA DIM is viewed with an XML browser However it is included in the META DATA DIM and can be found when the file is opened with an editor On the other hand the view angle is not included in the VOL_LIST PDF but is displayed with an XML browser applied to METADATA DIM e SPOT 4 5 imagery is usually delivered in the DIMAP format a tif file with the band sequence 3 2 1 4 NIR Red Green and 1 6 ym The wavelength increasing sequence has to be created before offering the file to ATCOR Attention Old SPOT 2 imagery are usually distributed in the CAP format For this old format the SPOT leader file and the voldir pdf indicate L instrument looks to west and R instrument looks to east This is a header coding error it is just vice versa so interpret L east R west 9 6 5 SPOT 6 SPOT 6 has 4 multispectral VNIR bands bl
227. hbroom linear array can perform the same task without moving optical elements but the number of array lines each recording a certain spectral channel in the focal plane is limited The third imaging technique employs an area detector array where one direction collects the spatial information across track and the orthogonal direction covers the spectral di mension The advantage of the last two techniques is a longer pixel dwell time and a potentially improved signal to noise ratio SNR The drawback is a substantial increase in the spectral and radiometric characterization i e a change of the channel center wavelength across the columns of the array spectral smile spatial misregistration keystone and detector non uniformity problems 57 20 79 Typical representatives of the whiskbroom type are Landsat TM ETM HyMap AVIRIS and Daedalus These instruments almost show no spectral smile 1 e the channel center position and bandwidth do not depend on column pixel location Spaceborne hyperspectral instruments showing the smile effect are Hyperion and CHRIS Proba airborne instruments are for example CASI 1500 and APEX This section describes the ATCOR input files required for smile sensors There are only two changes compared to the non smile instruments e The sensor definition file e g sensor_chris_mode3 dat needs one more line see Table 4 3 containing the parameters ismile 1 if smile sensor othe
228. he default output file name without the output keyword specification is the input name with an atm bsq appended e g data2 project1 imagel_atm bsq CHAPTER 6 BATCH PROCESSING REFERENCE 127 The keyword output can be used to specify the full output name or only the output path the latter option is recommended In that case all output files are written to the specified output directory and the reflectance output file name is the name of the input file with atm bsq appended Example output data4 project1 then the output reflectance file will be data4 project1 imagel_atm bsg The corresponding tile program atcor2_tile in this example is called to split the image into 3 sub images in x direction and 2 in y direction compare chapter 5 4 12 The optional keyword vis can be used to overwrite the visibility value in the inn file For a constant visibility per scene npref 0 in the inn file the input vis value is the start value that will be iterated as described in chapter 10 4 1 In case of a variable scene visibility npref 1 the vis parameter is ignored if the scene contains enough dark reference pixels If not the program switches to the constant visibility mode and vis is used as a start value An optional keyword output can be used to define the output directory and name of the output reflectance file If the keyword specifies only the output path whi
229. he Help menu allows browsing of the ATCOR user manual provides a link to web resources and displays license and credits information and give access to online software updates and components see chapter 5 9 4 2 First steps with ATCOR The 4TCOR button of Fig 4 5 displays the choices ATCOR2 multispectral sensors flat ter rain and ATCOR3 multispectral sensors rugged terrain If the add on for user defined mainly hyperspectral sensors is included then the buttons ATCOR2 hyperspectral sensors flat terrain and ATCORS8 hyperspectral sensors rugged terrain will also appear compare Fig 4 5 The last button starts the ATCOR processing in the image tiling mode i e the image is divided into sub images in x and y direction as specified by the user This mode is intended for large scenes compare section 5 4 12 and the inn file with the processing parameters must already exist Satellite ATCOR File Sensor Topographic ATCOR PRDF Filter Simulation Tools Help Licensed for Daniel Haze removal original DN data c DLR ReSe 2015 ATCOR21 pre defined sensors flat terrain ATCORS pre defined sensors rugged terrain ATCOR2 user defined sensors flat terrain ATCORS user defined sensors rugged terrain Start ATCOR Process Tiled from inn Landsat 8 TIRS surface temperature Figure 4 5 Top level graphical interface of ATCOR Atmospheric Correction Let us start with a scene from a
230. he BRDF 54 The NDVI is increased in Equation 10 132 by a value of up to 0 5 using the fact that dense agricultural vegetation shows higher green reflectance than dense forests i e the NDVI is increased by Cforest for dense forests having a green reflectance in a range below 7 In a further step the BCI is decreased for soils by Csoils using the effect that soils show a relatively low blue at sensor radiance A last adaption Cwater is made for water such that clear water areas are always set to a minimum value BCI NDVI C forest Uso Caio ez 10 132 Note the gt sign denotes a maximum operator between the left and the right side of the term The three correction functions in Equation 10 132 are given as follows first for forests using the absolute HDRF value in the green pgreen 0 5 ar ga 10 07 pgreendo go IN DVI 0 55 9 00 10 133 C forest The upper and lower values at the square brackets indicate a truncation at these values The upper values could be adapted for better representation of biome types For surface covers having a BCI below 0 1 i e mostly soils a reduction factor is found from the relation between blue and red HDRE as a3 E 1 10 NDVI Crorest lo 90 10 134 CHAPTER 10 THEORETICAL BACKGROUND 236 This factor accounts for the variability of non vegetated areas in the visible Finally a summand to account for water is added startin
231. he default name is test cal However it is recommended to include the name of the ground target here Now the target s can be clicked in the zoom window that pops up automatically Target 1 has to be clicked with mouse button 1 mb1 left target 2 with mouse button 2 mb2 center The zoom window is moved in the main window by pressing mb1 for target 1 and mb2 for target 2 Alternatively the target coordinates x y column line can be specified In addition to the file xxrxr cal the files xxx rdn radiance versus digital number and xxx adj original and adjacency corrected DN s are automatically created Select displey bands ifile ET z Visibility ka 15 0 iba File ierra Fed a la Green E la Blue 4 l r 1 21 mr ore or rik rervie Display dea ERA Hacerse r box canter coordinates fy ISL Figure 5 37 Radiometric CALIBRATION module The appearance of the inflight calibration module is similar to the SPECTRA module In the left part the image is loaded A zoom window can be created and two contrast stretching options Gaussian and histogram equalization are available In the right part two windows are provided to display the cl spectrum and the box averaged target DN spectrum The ymin ymax widgets allow the user to scale the graphical display The parameters visibility and adjacency range can be varied and their influence on the calibration curve can be studied CHA
232. he range 0 1 of the float illumination is then re scaled to 0 1000 to be compatible with the matched filter de shadowing case only for the purpose of de shadowing not for the correction of terrain effects Chapter 5 Description of Modules For most ATCOR modules a convenient graphical user interface is available but batch jobs can also be submitted If the atcor binary atcor sav is opened by the IDL virtual machine or when atcor is typed on the IDL command line a menu with pull down buttons pops up see Figure 5 1 with a thematic grouping of modules A detailed discussion of the interactive panel driven modules is given hereafter whereas a description of the batch commands can be found in chapter 6 000 Satellite ATCOR File Sensor Topographic ATCOR ERDF Filter Simulation Tools Help Licensed for Daniel Version 9 0 0 tc DLR ReSe 2015 Figure 5 1 Top level menu of the satellite ATCOR 49 CHAPTER 5 DESCRIPTION OF MODULES 50 5 1 Menu File The menu File offers some collaborative tools for handling of the data and ENVI files Below a short description of the individual functions is given Satellite ATCOR File Sensor Topographic ATCOR ERDF Filter Simulation Tools Help Display ENVI File Version 9 0 0 tc DLE FeSe 2015 Show Text File Select Input Image Resize Input Image import Geo TIFF Export RGBN Geo TIFF Plot Sensor Response NRGB Geo T1FF Plot Calibration File TPEG2000 Geo Re
233. hows an example in terms of the DEM illumination The top image is obtained after low pass filtering the original elevation file the central image is the illumination based on the original DEM and the bottom shows a 100 pixel transsect of the original elevation data revealing the steps The original DEM had a resolution of 30 m was coded as 16 bit integer and initially resampled to the 6 m pixel size of the image with CHAPTER 5 DESCRIPTION OF MODULES 7 INPUT IMAGE FILE Vexport datadatar atcor2 3 demo_data tm_flat tm_essen1000 bsg Date ddemme year 2008 1989 OUTPUT IMAGE FILE Fexport data data atcor2 3 demo_data tm_flat tm_essenLO00_atm bsq OVERWRITE Scale Factor 4 0 Help Sarei lake s Sapp epee ee Band selection Selected SENSOR Landsat 4 5 TH Select MH Z Pixel size m 80 0 CALIBRATION FILE Yexport datadatar atcor 3 cal landsatd_5 tm_standard cal ATMOSPHERIC FILE aamsrura ATM FILE for thermal bandis midlat_summer Adjacency range km 1 00 Help Zones E Visibility km 19 1 Solar zenith degree 343 0 Ground elevation km 30 1 SPECTRA AEROSOL TYPE VISTB ESTIMATE INFLIGHT CALIBRATION Help WATER VAPOR IMAGE PROCESSING Output file already exists change name or press OVERURITE MESSAGES QUIT Figure 5 31 ATCOR panel integer arithmetic After reprocessing the elevation file the other DEM derived files should also be reprocessed The pixel size of the DEM files must be the same as th
234. ht vegetation b4 b3 gt 3 0 and b2 b3 gt 0 8 or b3 lt 0 15 and b4 gt 0 45 e dark vegetation b4 b3 gt 3 0 and 02 03 gt 0 8 or b3 lt 0 15 and b3 lt 0 08 and b4 lt 0 28 e yellow vegetation b4 b3 gt 2 0 and b2 gt b3 and b3 gt 0 08 and b4 b5 gt 1 5 e mix veg soil 2 0 lt b4 b3 lt 3 0 and 0 05 lt b3 lt 0 15 and b4 gt 0 15 e asphalt dark sand b4 b3 lt 1 6 and 0 05 lt b3 lt 0 20 and 0 05 lt b4 lt 0 20 and 0 05 lt b5 lt 0 25 and b5 b4 gt 0 7 e sand bare soil cloud 64 63 lt 2 0 and 64 gt 0 15 and b5 gt 0 15 e bright sand bare soil cloud b4 b3 lt 2 0 and 64 gt 0 15 and b4 gt 0 25 or 65 gt 0 30 e dry vegetation soil 1 7 lt b4 b3 lt 2 0 and b4 gt 0 25 or 1 4 lt b4 b3 lt 2 0 and b7 b5 lt 0 83 e sparse vegetation soil 1 4 lt b4 b3 lt 1 7 and b4 gt 0 25 or 1 4 lt b4 b3 lt 2 0 and b7 b5 lt 0 83 and b5 b4 gt 1 2 e turbid water b4 lt 0 11 and b5 lt 0 05 e clear water b4 lt 0 02 and b5 lt 0 02 e clear water over sand b3 gt 0 02 and 63 gt b4 0 005 and b5 lt 0 02 Figures 5 70 and 5 71 show the panel of the SPECL program and a sample output 5 8 3 SPECL for User Defined Sensors This function is the same as described above in Section 5 8 2 and Figure 5 71 The only difference is that the available sensors are the ones defined in the sensor directory of the ATCOR installation i e the user defined sensors instead of the pre d
235. i e a haze optimized transform HOT can be defined as Zhang et al 2002 HOT BLUE x sina RED x cosa 10 97 4 Calculation of the histogram of HOT for the haze areas CHAPTER 10 THEORETICAL BACKGROUND 218 5 For bands below 800 nm the histograms are calculated for each HOT level j The haze signal A to be subtracted is computed as the DN corresponding to HOT level j minus the DN corresponding to the 2 lower histogram threshold of the HOT haze areas The de hazed new digital number is see figure 10 17 DN new DN A 10 98 A A 2 0 fs 5 Celta Pa D DN red band KH gt BJ Go Z 72 BO 83 80 At 43 od zir sh DN blue bond HOT level Figure 10 17 Haze removal method Left regression between red and blue band for clear areas Right calculation of Delta A as a function of the HOT haze level example Landsat TM band 1 So the haze removal is performed before the surface reflectance calculation Two options are available the use of a large area haze mask eq 10 99 which is superior in most cases or a compact smaller area haze mask eq 10 100 HOT gt mean HOT 0 5 x stdev HOT 10 99 HOT gt mean HOT 10 100 In addition the user can select between haze removal of thin medium haze or thin to moder ately thick haze the last option is superior in most cases The algorithm only works for land pixels so the near infrared band NIR is used to exclude water
236. illustration purposes 5 1 7 Plot Sensor Response In the panel Plot Sensor Response one may select the respective sensor response from within the available response functions in the ATCOR installation or elsewhere When selecting a response file the related bands are loaded automatically and the total number of bands is displayed The band range for display can be adjusted manually afterwards Normalization of the curves can be such that the area below the curves is constant same weight of the functions or the maximum is at 1 for all curves The displayed graph may be adjusted in appearance and size and finally being exported to a standard graphics file for further use CHAPTER 5 DESCRIPTION OF MODULES 96 2200 XiSensorResponse Viewer Choose any rep file to diplay the related series of response curvest Select Sensors Response Yerc_id atcor atcor_23 sensor aster14_hs aster01 rsp Channele Bands from i gt i E Em g E Normalization of Response to Area Tefault ave to Maximum PA NOD Xx ATCOR Sensor Response Plot File Font_Size Display Output Help Response from asterd rsp IN Wavelength Rasponas Figure 5 7 Plotting the explicit sensor response functions 5 1 8 Plot Calibration File When selecting this function the dialog defaults to the atcor installation for the selection of a x cal file to be displayed Both gain and offset are then plotted in the same graph to get an overview of their rel
237. ilted in the across track direction The nadir spatial resolution is 6 5 m The xml metafile contains information on the solar elevation angle illuminationElevationAngle solar azimuth illuminationAzimuthAngle and the view geometry i e the acrossTrackInci denceAngle and the view azimuth azimuthAngle ATCOR requires the sensor tilt angle 0y which is close to the across track incidence angle 6 on the ground The exact calculation can be done with eq 9 9 using the RapidEye orbit height 630 km 9 6 14 GeoEye 1 GeoEye 1 provides optical data with four multispectral channels in the 480 840 nm region with a spatial resolution of about 1 7 m In addition panchromatic data with a resolution of about 0 5 m is available The radiometric encoding is 11 bits per pixel The metafile for each scene contains the radiometric offset and gain values These values are given in the same unit as used by ATCOR i e mWem sr um gt so they can be directly taken i e cy Gain 9 14 The Offset cy is usually zero 9 6 15 World View 2 WorldView 2 provides optical data with 8 multispectral channels in the VNIR region 428 923 nm at a spatial resolution of 1 8 m nadir with a dynamic range of 11 bits per pixel The instrument has selectable radiometric gain factors absCalFactor specified in the metafile IMD The offset cy is zero for all channels and the gain c for ATCOR has to be calculated as
238. image dist size of smoothing filter outfile name of output file CHAPTER 6 BATCH PROCESSING REFERENCE 134 KEYWORDS median use median filter instead of default low pass filter e bil_2_bsq infile outfile e bip_2_bsq infile outfile Conversion of ENVI band interleaved by line BIL or by pixel BIP to ATCOR standard band sequential storage order BSQ PARAMETERS infile input data cube in BIL or BIP format ENVI image outfile name of output file to be created optional default _img bsq e multires sensor sensor PSeNS PSENS PSPEC PSPec Resampling of multiple spectra stored as separate ASCII files dat in the path pspec The spectral channel response functions rsp are stored in the path psens and the keyword sensor specifies the sensor name The ASCII files dat contain 2 columns wavelength um or nm spectral value Empty lines in between or at the end are not allowed The spectrum can have any unit radiance reflectance emissivity Example multires sensor xxx psens data7 sensor xxx pspec data7 spectra The resampled spectra are written to a new folder data7 spectra xxx e multires_slb sensor sensor psens psens slbfile slbfile Same as multires but all input spectra are stored in a single ENVI spectral library file slb and the keyword slbfile specifies this file with path Example multires_slb sensor xxx slbfile d
239. imple empirical equation for the surface reflectance in the red band ARVI Av ed 0 01 0 03 Pred T A Ap 10 87 In reality this relationship is dependent on biome season and geography 86 Corresponding maps have been developed for the MERIS sensor using a coarse reolution of about 3 km x 3 km but these are not appropriate for high spatial resolution instruments Finally the dark reference pixels from both VNIR approaches eq 10 84 and 10 87 are combined Visibility and aerosol optical thickness The visibility calculated for each reference pixel range 5 190 km in ATCOR is converted into an integer called visibility index vi with range 0 182 The visibility index is closely related to the total optical thickness 6 at 550 nm the equidistant optical thickness spacing is 0 006 for a ground at sea level and smaller for increasing elevations 0 185 0 006 x vi 10 88 It is easy to calculate the aerosol optical thickness AOT from a known total optical thickness by subtracting the Rayleigh optical thickness and a very small trace gas optical thickness compare Fig 2 1 in chapter 2 With the MODTRAN code the AOT at 550 nm can be calculated from a given visibility VIS km as AOT exp a z b z In VIS 10 89 where z is the surface elevation and a z b z are coefficients obtained from a linear regression of In AOT versus In VIS CHAPTER 10 THEORETICAL BACKGROUND 214 e Total
240. in an optimization procedure that minimizes the spikes of the derived surface reflectance spectra in the atmospheric absorption regions The first target DN file has to be entered at the top left button of the GUI panel Figure 5 73 The other target files are automatically searched and employed if the nomenclature of chapter 2 2 is employed Further input data are the sensor definition the range of bands per spectrometer solar geometry and atmospheric parameters Output is a file with the spectral channel center wavelength shifts per spectrometer and a new wavelength file containing the updated wavelengths for each channel The results of the spectral shift are summarized in a file spectral_calibration_results tat where the wavelength shift is listed for each spectrometer and each target The final shift is taken as the average of all target wavelength shifts In addition a new wavelength file sensor_new wvl is created containing the channel center wavelengths and the FWHMs bandwidth as full width at half maximum A copy of the original radiometric calibration file e g xxx cal is provided for convenience e g xxx_new cal which contains the original radiometric calibration coefficients and the updated channel center wavelengths In case of originally non Gaussian filter curves the output FWHM values of sensor_new wul represent the equivalent Gaussian FWHM values even though the spectral re calibration is base
241. inctly smaller path radiance The water threshold for a 1600 nm band is always included as the second criterion if such a band exists Sometimes the gradient criterion with eq 9 3 is not adequate and the NIR SWIR1 reflectance thresholds yield a better water mask This may happen in urban areas containing shadow pixels cast by buildings Then the NIR SWIR1 thresholds have to be defined as negative reflectance values to overwrite the gradient criterion lt 0 for 0 4 lt A lt 0 85 um 9 3 The band interpolation options are only intended for hyperspectral imagery Linear interpolation is employed in the 760 725 and 825 nm regions Non linear interpolation as a function of the veg etation index is applied in the 940 and 1130 nm parts of the spectrum to account for the leaf water content in plants nterpolation in the strong atmospheric water vapor absorption regions around 1400 nm and 1900 nm is recommended because of the low signal and large influence of sensor noise CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 161 However interpolation can be disabled if required i e for test purposes If enabled non linear interpolation is performed in the 1400 1900 nm regions by fitting the surface reflectance curves with a hull of a template vegetation or soil spectrum All interpolated channels are marked with an in the ENVI header of the output reflectance cube Haze or sunglint removal over water the default apparent
242. ing been calculated beforehand and the need to be calculated consecutively e g calibl rdn calib2 rdn First rdn file Name of the first rdn file of the series to be used for regression Output Name Name of the calibration output to be created Output The output of this program is an ASCII file name cal with three columns center wavelength co c1 where name is specified by the user Note If several calibration targets are employed care should be taken to select targets without spectral intersections since calibration values at intersection bands are not reliable If intersections of spectra cannot be avoided a larger number of spectra should be used if possible to increase the reliability of the calibration CHAPTER 5 DESCRIPTION OF MODULES 119 5 8 8 Convert High Res Database New Solar Irradiance The standard solar irradiance curve used with ATCOR is the Fontenla database 2011 However there s some uncertainty about the solar irradiance and people may want to use ATCOR with a different solar reference function This module CONVERT_DB3 converts the complete high resolution atmospheric database from the current definition to a new irradiance function Normally the standard database is converted this function does not apply to the thermal IR but also the specific CHRIS database may be selected In the panel see Fig 5 75 the two databases may be selected on the basis of the directory f1 and the
243. into details a brief overview of the main processing steps during atmospheric cor rection is described in the next two flow charts Figure 10 1 contains a compact summary of the main processing elements after reading the sensor specific LU T s a masking and preclassification is conducted to obtain land water haze cloud and shadow areas Then an optional haze or cirrus re moval is conducted followed by an optional shadow removal The next step calculates the visibility or aerosol optical thickness map using the dense dark vegetation DDV method This is followed by an update of the aerosol model path radiance behavior in the blue to red bands if a blue band exists and the update option ratio_blu_red 0 is enabled Otherwise the selected aerosol model is not changed After calculation of the water vapor map the iterative surface reflectance retrieval is conducted accounting for adjacency and spherical albedo effects After atmospheric correction a spectral polishing for hyperspectral instruments and BRDF correction might be performed The visibility AOT 550 nm retrieval flow chart describes the case with a SWIR band at 2 2 um It starts with a low reflectance threshold T1 0 05 and performs the masking in this SWIR band to obtain the darkest pixels excluding water If the number of reference pixels is less than 2 of the scene pixels then the threshold T1 is increased until threshold T2 0 12 is reached If not 183 CHAPTER 10 THEORE
244. iometric calibration e sapada a 21 Oe DEF os a aa Se HE a a a 22 29 BROF POLEO soe seas aa AAA 24 3 Basic Concepts in the Thermal Region 28 4 Workflow 30 Al Menus Overview simo 30 4 2 First steps with ATCOR 2 s nisin tk driki ajarin 32 4 3 Survey of processing steps we Kh e e AA 35 AA Directory structure of ATCOR 2 24 44 268646 a 36 4 5 Convention Tor le names 2 4 66 ee hee eee RE OREM ERS RE Ewy pE 37 4 6 User defined hyperspectral sensors a a a 39 4 6 1 Definition of anew season 40 A Spectral smile sensors c 20o lt lt ee bebe ee wee dew sa 4 4S Haze COMO water Map sicario 43 4 9 Processing of multiband thermal data a 45 4 10 External water vapor map 48 4 11 External float illumination file and de shadowing a 48 5 Description of Modules 49 ma MESES ceci EES ES SHEERS He 50 Ll Display ENVLETDS ssere reddis ee 4 G Ee aa 50 ska Dhow US IEEE 53 odo Resize Input Mape o a e ereer ew ee yE AE A A 53 Oem Select lnput Image ATEN 54 kko PI sanar asas 54 lar Bi sao oa sa Aa AAA ew 59 5 1 7 Plot Sensor Response ooo a Es 55 DLS Plot Can Fle cocer rss 56 5 1 9 Read Sensor Meta Data a 56 DIO Show System Pile 225 cee an bbe de wee badr aa 56 5 1 11 Edit Preferences isc Hh ORR E ESE a Ee EH dS 57 CONTENTS 9 2 5 3 5 4 5 9 5 6 5 7 5 8 Menu Dad 9 2 2 Deu sae peas Menu Dank edad ei 5 3 4 5
245. ion After calculation of the scene path radiance in the blue 0 48 um and red 0 66 um region as total minus reflected radiance using the average values obtained for the dark reference pixels the ratio of L blue scene to Ly red scene can be compared to the corresponding ratio for the MODTRAN standard aerosols rural urban maritime desert q Lp blue scene Ly red scene 10 82 P Lp blue MODTRAN L red MODTRAN The aerosol type for which the double ratio d is closest to 1 is the best approximation for the scene It approximates the corresponding MODTRAN aerosol type However some fine tuning is subsequently performed to be able to modify the wavelength behavior of the path ra diance compared to the standard aerosol types If L blue scene deviates more than 5 from Lp blue MODT RAN then L blue scene is used as the valid path radiance In addition the path radiance for any other bands in the blue to red region is linearly re scaled with the factor Lp blue scene Ly blue MODT RAN see Figure 10 13 Here the path radiance in the red band is used as a fixed tie point For wavelengths greater than 700 nm a posssible typical 10 differ ence in path radiance between the selected aerosol type after fine tuning and the actual aerosol is usually not important because path radiance contributes only a small fraction to the total radiance If the sensor has no blue spectral band but a green band than the gre
246. ion repeat values resolution 0 02 nm Combine the two files for VNIR and SWIR manually into one file afterwards 3 Using the same sensor as above run the atmospheric correction with the smile correction option switched ON after putting the new file smile_poly_ord4 dat into the sensor definition directory 4 Apply the spectral polishing routine see Section 5 6 3 and 5 6 4 and 5 Run the Spectral Smile Interpolation module see Section 5 6 7 on the atmospherically cor rected image 4 8 Haze cloud water map Although the surface reflectance cube and temperature emissivity for thermal channels is the main result of the atmospheric correction some additional products are often requested One of these products is a map of the haze cloud water and land pixels of a scene This map not only delivers a basic scene classification but it may also contain information about potential processing problems For example if turbid water pixels are not included in the water mask the haze mask may also be not appropriate and consequently results of the haze removal over land might be of poor quality Such a pre classification as part of the atmospheric correction has a long history 24 42 64 65 66 43 53 It is also employed as part of NASA s automatic processing chain for CHAPTER 4 WORKFLOW 44 MODIS 1 using the classes land water snow ice cloud shadow thin cirrus sun glint etc Therefore the calculat
247. ion above to make the changes e A sensor definition file must be specified this is also done with the routine Define Sensor Parameters If you re about editing the sensor manually you may just copy any of the existing files e g sensor_chris m1 dat and modify the appropriate lines see the next table e A wavelength file wvl or an ENVI header file hder containing center wavelength and FWHM information has to be specified The wvl file is a simple ASCII file with three columns band number center wavelength and bandwidth compare Fig 5 13 Center wavelength and bandwidth may be given in the nm or um unit The first line may contain an optional header with text This wavelength file or the ENVI header file will be used to create the spectral response function for each band as a numerical table the band rsp files compare Fig 5 13 Eight analytical filter shapes can be selected from the top level graphical interface Fig 4 1 when selecting the menu Sensor then choose the Create Channel Filter Files button Then the menu of Fig 4 14 will pop up and one of these 8 filter shapes can be selected Filter numbers 1 to 4 are of the Butterworth type the slow drop off for the Butterworth order 1 is truncated at the 0 05 response and set to zero The filter type 9 parameter filter_type in Table 4 3 is reserved for arbitrary user specified channel filter functions the band rsp files e
248. ion of eq 10 10 is R p a y p x y alo z y J EO WA exp r r5 ar 10 11 0 Here R is the range where the intensity of the adjacency effect has dropped to the 10 level i e r R 2 3x rs where r is a scale range typically r 0 2 0 4 km R 0 5 1 km p r is the reflectance at range r from the x y position and A r is the area of a circular zone from r to r dr Now we approximate the circular regions by square regions to obtain the discrete version of eq 10 11 with exponentially decreasing weighting coefficients wi NR plz y ph x y a e e y X wi 10 12 a 1 CHAPTER 10 THEORETICAL BACKGROUND 189 Wi api and W J A rjexp r dr reap r ar 10 13 2 Wi Ti l1 Ti 1 E ATCOR supports up to nr 5 regions Since the sequence of moving digital low pass filters works with square filters of size 2r 2r the area A r is approximated as the corresponding square region A r 2r Step 3 it includes the spherical albedo effect on the global flux that was initially calculated with the reference background reflectance pp 0 15 and is finally adapted to the scene dependent value p by correcting with the difference p pr p x y p x y 1 a x y pr s 10 14 Radiation components in rugged terrain Figure 10 5 shows a sketch of the radiation components in a rugged terrain 68 Compared to the flat terrain one additional component is
249. is used for the meta data information It should be named according to the ATCOR convention and situated at the same folder as the _atm file Files are added using the Add File s button on top while entries may be removed by the Remove Entry button CHAPTER 5 DESCRIPTION OF MODULES 95 e00 X BREFCOR v 1 0 c ReSe 2013 Input Files Add File s Remove Entry cubes wor1dview dlr_brdf P001_MUL 12JUL22020705 M245 052751439010_01_P001_atm bsq cubes wor1dview dlr_brdf P006_MUL 12JUL22020629 M2A5 052751439010_01_P006_atm bsq _ Roujean Geometric Kernel l Maignan Hot Spot Geometry Spectral Smoothing of Model Model Options _ Model Interpolation Use Precalculated Model F Write ANIF Outputs Calibration Granularity w Coarse 4 Classes Standard 5 Classes w Fine 6 Classes w Hyperspectral TBD Reflectance scale factor from 0 1 10000 0 Select Output Directory Ycubes uor1dview Select Output BRIF Model File Name brefcor_nodel saw Output File s Appendix _bcor bsd Console see model log file E E L BRR RAR AAR ERE RR ER AEE EER ER EE REE EERE ERE ER EE BR ai BREFCOR Version 1 0 Beta created by ReSe Applications Schlaepfer SRE RR OE EE OE EE o od od o o o o EO EO OO Version Release and Credits BREFCOR Version 1 0 Beta authored by Daniel Schlaepfer ReSe Help Calibrate Model Run Model Based Run Empirical Done Figure 5 53 BREFCOR corre
250. isibility and perhaps the atmospheric water vapor column wv before processing the image cube The SPECTRA module can be employed for this purpose see chapter 5 4 6 Reflectance spectra of scene targets can be displayed as a function of visibility and water vapor the winter fall summer and tropical atmospheres have different wv contents see appendix A and compared with field or library spectra If calibration problems exist in a few channels a copy of the calibration file can be edited in these channels to match the reference spectrum If there are problems in many channels the inflight radiometric calibration module should be used to generate a calibration file as discussed in chapter 5 4 9 When the calibration file is OK the user can continue with the image processing Depending on the available sensor channels there are options to process the imagery with constant or variable visibility and atmospheric water vapor For large FOV sensors an option is available to correct for across track illumination BRDF effects This is especially useful if the image recording took place in the solar principal plane In addition a spectral polishing can be performed for the CHAPTER 4 WORKFLOW 36 Load Image y Define Sensor SPECTRA scene spectrum gt reference spectrum l l p Inflight calibration j ae gt astron Figure 4 9 Typical workflow of atmospheric correction atmospherically and or BRD
251. ive ATCOR session When all image processing parameters have been defined by the user this 2nn file is written to the directory of the corresponding image When the image is re loaded during a later session or when a batch job is submitted all input parameters are read from this file It is suggested not to edit this file because it might create inconsistent input data The file might contain empty lines when input is not required for a specific case The IDL routine for writing this file is available on request for users who want to run ATCOR batch jobs without employing the interactive GUI panels The contents of an inn file are line 1 20 08 1989 Date dd mm year i e 20 08 1989 occupies the first 10 columns line 2 100 0 scale factor reflectance 0 100 range The default reflectance scale factor in the output file is s 100 for 16 bit data and s 4 for 8 bit data i e a reflectance value of say 21 75 is coded as 2175 and 87 for s 100 and s 4 respectively For 8 bit input data and s 4 reflectance values above 63 75 are truncated at 255 If higher values are expected a scale factor of s 100 should be selected For s 1 the output file will be coded as CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 162 32 bit float data requiring twice the disk storage as for s 100 With s gt 0 negative reflectance values are truncated to 0 With s 100 or s 1 the negative values are not truncated This option may some
252. k cirrus cloud T he two cirrus cloud classes are merged with the normal cloud class for the surface reflectance retrieval during the treatment of the adjacency effect here the reflectance of the cloud pixels is replaced with the average reflectance of the non cloud pixels to avoid an overestimation of the adjacency effect The classes are currently defined with the following criteria CHAPTER 10 THEORETICAL BACKGROUND 202 O geocoded background shadow thin cirrus over water thick cirrus over water land saturated blue green band snow ice thin cirrus over land 9 medium cirrus over land thick cirrus over land thin haze over land 12 medium haze over land thin haze over water 14 medium haze over water cloud over land cloud over water cirrus cloud cirrus cloud thick Table 10 1 Class labels in the hew file Water class If the surface elevation of a pixel is lower than 1 2 km above sea level then a water pixel has to fulfill the criteria p blue lt 0 20 and p blue gt p green 0 03 po NIR lt p green and p 1 6m lt Tuvater 3WIR1 10 48 where Tivater swIr1 is the water threshold reflectance in the SWIR1 band around 1 6 jum as de fined in the preference parameter file see chapter 9 3 Basically the gradient of the apparent water reflectance has to be negative If the pixel elevation is higher than 1 2 km the criterion of a negative gradient for the apparent re flectance does
253. lative amount of the unobstructed sky hemisphere This factor is highly variable on small scales e g in vicinity of forest borders The skyview factor is approximated from the cast shadow fraction such that all areas in complete cast shadows get a reduced skyview factor as Os 0s Vs he dhad lt 100 Vs eom 10 21 tu U Tago fshadTggo 100 lt Vat aaa CHAPTER 10 THEORETICAL BACKGROUND 193 where 0 is the solar zenith angle and the geometrical skyview factor Vsky geom had been calculated based solely on the digital terrain model Application in ATCOR Workflow In ATCOR 4 the local illumination angle y is first calculated on the basis of the terrain model using an efficient vector algebra based method The topographically corrected reflectance Propo 18 then retrieved from the atmospherically corrected ground leaving excitance Mg using the direct irradiance Tair the diffuse illumination field qif and the terrain illumination ter as My Fo a NA AP ca IN naar 7 de 10 292 Taincos p 0 11q pcos y 0 91 Very Leer 10 22 Ptopo where the factors 0 1 and 0 9 account for the relative amount of circumsolar irradiance estimated to be 10 of the total diffuse irradiance The local illumination factor cos y is now enhanced by the cast shadow fraction such that shaded areas are not affected by direct and circumsolar irradiance Figure 10 8 Effect of combined topographic cast shadow correction left
254. le text editor to edit plain text ASCII files 5 1 3 Resize Input Image This tool allows to resize some georeferenced ATCOR input data to new spatial dimensions without resampling The following inputs are necessary Inputs Files e Input Image Name name of input data file to be resized e Input DEM Name name of input elevation data file this line may be left empty if no DEM is available Dimensions e Upper Left corner coordinates of upper left corner pixel ENVI convention lower left edge of upper left corner pixel is pixel 1 1 e Use Reference File the coordinates of a reference file may be used e Number of Pixels size of output file in pixels in x and y direction ncols nrows starting at upper left corner e Output name Basis name of output main file auxiliary files are stored according to ATCOR conventions i e _ele bsq etc CHAPTER 5 DESCRIPTION OF MODULES 94 Output Based on the output Name Basis the auxiliary files are stored according to ATCOR conventions 1 e _ele bsq etc Possible outputs are e at sensor radiance image ATCOR main input image e scan angle file _sca bsq if available e illumination file _ilu bsq if available e elevation file if set e side layers of topography _slp _asp _sky 000 Ix Resize an ATCOR data set Select Input Image Name Yerc_id atcor atcor_4 demo_data Hyspex_ATCOR_demo FL3_YNIR_geo bsq UTM 1 1 593455 750 6285600 250
255. les are specified in the METADATA DIM but the tilt angle is input to ATCOR The tilt view angle is not included in old versions of METADATA DIM but was added later For a given incidence angle the corresponding tilt view angle can be calculated as R Oy aresin 7 sin 91 180 7 9 9 where Rpg 6371 km is the earth radius and h 832 km is the SPOT orbit altitude Example incidence angles of 5 10 20 30 correspond to view angles of 4 4 8 8 17 6 and 26 2 re spectively IncidencSpy ll cn t Figure 9 9 SPOT orbit geometry In addition to the tilt angle the view direction with respect to the flight path is specified Nearly all SPOT data 99 9 is recorded in the descending node i e flying from the north pole to the equator indicated by a negative value of the velocity vector for the Z component in the METADATA DIM Then a positive incidence tilt angle in METADATA DIM means the tilt direction is left of the CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 177 flight direction east for the descending node This is indicated by an L in the incidence angle in VOL_LIST PDF e g incidence angle L20 6 degree A negative incidence angle means the sensor is pointing to the west coded as R right in the VOL_LIST PDF e g incidence angle R20 6 degree For ATCOR the satellite azimuth as seen from the recorded image has to be specified If a denotes the scene orientation angle wit
256. level and no aerosols in the boundary layer and higher atmosphere In this case the total optical thickness molecular and ozone is about 6 0 13 Since the optical thickness due to molecular scattering nitrogen and oxygen only depends on pressure level it can be calculated accurately for a known ground elevation The ozone contribution to the optical thickness usually is small at 550 nm and a climatologic geographic average 331 DU can be taken Nevertheless if scene information on ozone is available it can be specified as an input parameter This leaves the aerosol contribution as the most important component which varies strongly in space and time Therefore the aerosol optical thickness AOT at 550 nm is often used to characterize the atmosphere instead of the visibility i v 2 E E E 8 E i 2 05 total optical thickness 550 nm sea level 0 0 3 50 100 150 200 0 5 hake eS 2 0 25 Visibility km Wavelength ern Figure 2 1 Visibility AOT and total optical thickness atmospheric transmittance The atmospheric direct or beam transmittance for a vertical path through the atmosphere can be calculated as T e 2 4 Fig 2 1 right shows an example of the atmospheric transmittance from 0 4 to 2 5 wm The spectral regions with relatively high transmittance are called atmospheric window regions In absorbing regions the name of the molecule responsible for the attenuation of radiation is included Apparent
257. lity water pixels than medium probability pixels eq 10 68 If a SWIR1 band exists the apparent NIR reflectance thresh old is relaxed first line of eq 10 69 because of the additional SWIR1 surface reflectance threshold p NIR lt 0 05 and p SWIR1 lt 0 03 OT p NIR lt 0 03 or p SWIR1 lt 0 03 10 69 The relationships on the second line of eq 10 69 assign the water probability based on the smaller reflectance value in the NIR or SWIR1 CHAPTER 10 THEORETICAL BACKGROUND 207 e high water probability coded 90 same as for medium probability but with lower NIR SWIR1 thresholds p NIR lt 0 03 no SWIR1 band p NIR lt 0 03 or p SWIR1 lt 0 02 10 70 Note the default threshold Twater swIr1 is 0 03 or 3 in the reflectance percent unit defined in the preference parameter file Snow ice probability The criteria for the snow ice class are described in the previous section As mentioned before if pixels are saturated in the blue green spectral bands they are counted as cloud unless the N DSI gt 0 7 The following probability rules are employed for snow e low snow ice probability coded 30 p blue gt 0 22 and NDSI gt 0 4 and DN blue lt Tsaturation 10 71 If no blue band exists a green band is used as a substitute If a green band and a SWIR2 band exist the rules are DN blue lt Tsaturation and p blue gt 0 22 NDSI gt 0 4 or p green gt 0 22 NDSI gt 0 25 p SWIR
258. lly if the SPECL module is not employed The setting tiff2envi 0 is automatically replaced with tiff2envi 1 if the inn file specifies the de hazing or de shadowing option because these require additional disk files In case of tiling and a TIFF input file the program automatically switches to tzff2envi 1 because the inter mediate tile images must have the ENVI bsq format e atcor3_batch input filename output file vis vis tiff2envi tiff2envi or atcor3_tile input filename ntr 3 nty 2 output file vis vis tiff2envi tiff2envi The 3 in atcor3_batch means the code for rugged terrain i e a DEM is employed as well as other DEM related files e g slope aspect skyview Otherwise the same explanations hold as for the flat terrain ATCOR The corresponding tile program atcor3_tile in this example is called to split the image into 3 sub images in x direction and 2 in y direction compare chapter 5 4 12 The keywords output and vis are described in atcor2_batch above e Note optional keywords for atcor2_batch atcor3_batch atcor2_tile atcor3_tile There are four keywords concerning spectral interpolation to overwrite the interpolation set tings in file preference_parameters dat 1725 1 no interpolation for 725 820 nm channels i725 1 interpolation 1760 1 no interpolation for 760 nm channels i760 1 interpolation 1940 1 no interpolation for 940 nm channels i940 1 nonlinear in
259. lux is neglected i e w 1 The factor f is 1 for a flat terrain and some bounds were employed to prevent overcorrection So for each pixel the new digital number is calculated as DN new DN x f 5 3 The method was compared with the standard Minnaert correction eq 5 4 and was superior in most cases Figure 5 29 shows the GUI panel COSO iso ico 00595 A cosB y ff Figure 5 29 Topographic correction only no atmospheric correction CHAPTER 5 DESCRIPTION OF MODULES 76 5 4 Menu ATCOR The menu ATCOR contains the main processing modules of ATCOR i e the panels for ATCOR2 flat terrain and ATCOR3 rugged terrain Two modes are distinguished pre defined and user defined usually multispectral and hyperspectral sensors respectively In this section the main panels are first shortly described Thereafter the subroutines SPECTRA and IFCALI and all panels related to them are explained X Satellite ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Licensed for Daniel Haze removal original IN data ic DLR ReSe 2015 ATCOR2 pre defined sensors flat terrain ATCORS pre defined sensors rugged terrain ATCOR2 user defined sensors flat terrain ATCOR3 user defined sensors rugged terrain Start ATCOR Process Tiled from inn Landsat 8 TIRS surface temperature Figure 5 30 The Atm Correction Menu 5 4 1 The ATCOR main panel Figure 5 31 top shows the input paramet
260. m 5 Y 13 14 48 39 38 Table A 7 Altitude profile of the tropical atmosphere Total ground to space water vapor content 4 11 cm or g cm Appendix B Comparison of Solar Irradiance Spectra The following two plots show the relative differences between two extraterrestrial solar irradiance sources e Kurucz 1997 distributed with MODTRAN Berk et al 2008 8 The previous high resolution monochromatic databases of ATCOR were calculated with this spectrum e Fontenla 2011 Fontenla et al 2009 2011 22 23 The new ATCOR release uses the improved quiet sun spectrum of Fontenla and co workers also referred to as low activity sun As explained in chapters 5 8 8 5 8 9 the user can convert the database of atmospheric look up ta bles from one solar irradiance source to another one provided that the spectral range and sampling distance agrees with the template spectra in the sun_irradiance directory of ATCOR Currently irradiance spectra of Kurucz 1997 Kurucz 2005 distributed with MODTRAN 8 and Fontenla 2011 are offered The plots show the detailed information line structure contained in the Fontenla spectrum The curves with 2 8 nm and 10 nm represent results based on a moving average of the the 0 4 nm data over 7 and 25 spectral points respectively 293 APPENDIX B COMPARISON OF SOLAR IRRADIANCE SPECTRA Relative Difference 5 Relative Difference 5 black FWHM 0 4 nm g
261. me pixelsize 10 0 kernel 3 dem_unit 0 The filename should have the last four characters as _ele and the extension bsq Two output files slope and aspect are generated from the elevation file e g example_DEM25m_slp bsq and example DEM25m_asp bsq The values are coded in de grees The keyword pixrelsize is not required if this information is included in the map info CHAPTER 6 BATCH PROCESSING REFERENCE 126 of the ENVI header The keywords kernel and dem_unit can be omitted if the default values kernel 3 and dem_unit 0 are used The unit of pirelsize is meter For the elevation height unit three options exist dem_unit 0 height unit is meters 1 for dm 2 for cm Note Before running ATCOR with a DEM please check the results of the slope image We often encounter severe horizontal and vertical striping in the slope image in case of low quality DEMs or if coarse DEMs of 25 m have to be resampled to say 5 m Additional appropriate filtering is required in these cases A simple way might be to try a larger kernel size e g kernel 5 or kernel 7 A simple check on the derived DEM solar illumination file is also performed at the start of ATCOR see the discussion below A TIFF input elevation file is converted into the corresponding ENVI elevation file e skyview_batch input filename pixelsize 10 0 dem_unit 0 unders unders azi_inc azi_inc ele_inc ele_inc filename
262. me If makeblue is started on the IDL command line the input file name CHAPTER 6 BATCH PROCESSING REFERENCE 129 must be fully qualified i e the path has to be included The input has to be atmospherically corrected data i e an _atm bsq file where the blue band is missing e g imagery of SPOT IRS 1D DMC The output file contains a synthetic blue band as the first image channel calculated from the green red and NIR bands on a pixel by pixel basis The output file name is _blue_atm bsq This product is a useful supplement to be able to create a true color image of the scene The calculation of the surface reflectance in the synthetic blue band consists of three steps 1 The blue band reflectance index 1 is extrapolated from the green index 2 and red index 3 band reflectance p p2 P2 ps A2 A1 A3 Ag 6 1 where the center wavelength of the blue band is taken as Ay 480 nm This represents the typical spectral behavior of soils and some artificial surfaces asphalt concrete 2 The red and NIR index 4 bands are employed to compute the ratio vegetation index VI pa1 p3 Pixels with VI gt 2 5 and p4 gt 10 are defined as vegetation and the blue band reflectance is calculated as p p3 2 6 2 3 Finally water pixels are masked with the criterion p4 lt 7 where the large threshold of 7 is employed to account for potential turbid water bodies and the blue ba
263. measure the absorption depth see chapter 10 4 3 Otherwise if a sensor does not possess spectral bands in water vapor regions e g Landsat TM or SPOT an estimate of the water vapor column based on the season summer winter is usually sufficient Typical ranges of water vapor columns are sea level to space tropical conditions wv 3 5 cm or g em midlatitude summer wv 2 3 cm dry summer spring fall wv 1 1 5 cm dry desert or winter wv 0 3 0 8 cm 2 2 Spectral calibration This section can be skipped if data processing is only performed for imagery of broad band sensors Sensor calibration problems may pertain to spectral properties i e the channel center positions and or bandwidths might have changed compared to laboratory measurements or the radiometric properties i e the offset co and slope c1 coefficients relating the digital number DN to the at sensor radiance L co cy x DN Any spectral mis calibration can usually only be detected from narrow band hyperspectral imagery as discussed in this section For multispectral imagery spectral calibration problems are difficult or impossible to detect and an update is generally only performed with respect to the radiometric calibration coefficients see chapter 2 3 Surface reflectance spectra retrieved from narrow band hyperspectral imagery often contain spikes and dips in spectral absorption regions of atmospheric gases e g oxygen absorption around 760 nm wa
264. ment channel is required The advanced APDA method can take into account multiple absorption channels in the 810 820 nm 910 960 nm and 1110 1150 nm regions T wo water vapor retrieval algorithms are available in ATCOR compare chapter 9 4 parameter iwv_model 1 2 1 The water vapor maps with the smallest standard deviation in the 940 nm and 1130 nm region are selected Finally if both regions are available the average of these two water vapor maps is taken parameter iwv_model 1 in the inn file 2 A linear regression ratio LIRR is applied to multiple bands parameter iwv_model 2 This water vapor map might be more accurate because the regression reduces sensor noise and may partially compensate calibration problems in lucky cases Although the water vapor map might be less noisy the retrieved surface reflectance spectrum will always retain any channel calibration problems Remarks CHAPTER 10 THEORETICAL BACKGROUND 216 APDA Ratio water vapor column ern Figure 10 16 APDA ratio with an exponential fit function for the water vapor 1 The APDA algorithm is relatively fast Its disadvantage is that it is not stable numerically for very low reflectance targets water shadow regions The transmittance slope ratio method 82 might work better in these cases so it is an interesting alternative water vapor algorithm However since the required processing time is much higher than for the APDA method i
265. mperatures 10 K below and above air temperature and same as air temperature The shaded areas indicate a typical range of humidity water vapor conditions The surface temperature error in the shaded regions is smaller than 0 5 K US and 1 K MS For the MS atmosphere and water vapor columns ranging from 0 5 2 5 cm the error is also smaller than 0 5 K The surface temperature calculation independently of the OLI bands is available from the main AT COR GUI Atm Correction Landsat 8 TIRS surface temperature An optional user specified temperature offset can be added to match ground measurements if such data is available e g lake temperature The calculation can also be performed in the batch mode using the command e tirs temp input datal LC81920272013135LGNO01_B10 TIF or with the offset option example offset 1 2 K e tirs temp input datal LC81920272013135LGN01_B10 TIF offset 1 2 Figure 9 6 shows the corresponding surface temperature error for the same atmospheric conditions but with the spectral emissivity of water A temperature offset Toffset 1 has to be specified to obtain temperature retrieval errors smaller than 0 5 K The reason is that the spectral emissivity of water see Fig 9 7 is not constant in the thermal spectral region The last case presents the surface temperature error for a constant emissivity of 0 95 in the spectral region of B10 and B11 typical for asphalt see Fig 9 8 Here
266. n 0 2 0000 Top level graphical interface of ATCOR e Top level graphical interface of ATCOR File 0 0 004 Top level graphical interface of ATCOR Sensor 2 2 0 0 0 Topographic MOMMIES lt e s s es 4 oe seeds ERO HREM OH EDS ew ERE O Top level graphical interface of ATCOR Atmospheric Correction ATCOR panel for flat terrain imagery 0 ee a Image processing options Right panel appears if a cirrus band exists Panel for DEM es ko ee mw eee Re Re ee HO eG Typical workflow of atmospheric correction ee Input output image files during ATCOR processing Directory structure of ATCOR 2 aa a Template reference spectra from the spec_lib library Directory structure of ATCOR with hyperspectral add on Supported analytical channel filter types 20 0 00000 eee eee Optional haze cloud water output file o e e Path radiance and transmittace of a SEBASS scene derived from the ISAC method Comparison of radiance and temperature at sensor and at surface level Top level menu of the satellite ATCOR Te PIS NEM ooer casi sisas Band selection dialog for ENVI file display o a0 a a a a LIST OF FIGURES 9 4 9 0 9 6 5 7 5 8 Do 5 10 9 11 9 12 5 13 5 14 5 15 5 16 5 17 5 18
267. n full cast shadows all layers write all layers instead of the illumination file only e atshadowfilter infile ilufile outfile smfact interp meanadjust min Filter dark bright borders of shadows after cast shadow correction PARAMETERS reflfile file to be filtered reflectance file ilufile illumination file containing a shadow mask which had been applied to the image outfile output file of the processing KEYWORDS smfact width of shadows to be corrected interp shadow borders are replaced by interpolations instead of brigthness adjustment meanadjust The mean brightness across all spectral bands is adjusted not only band wise min minimum threshold for border pixel detection default 0 02 e at_scalefwhm sensorin sensout_dir fwhmdat meanshift CHAPTER 6 BATCH PROCESSING REFERENCE 133 e at_shiftresp sensorin sensout_dir smiledat meanshift Apply FWHM or smile detection results to a sensor PARAMETERS sensorin input sensor definition file sensor dat sensout_dir output sensor name to be created path to directory in sensor directory of atcor installation fwhmdat smiledat files smile_poly_ord4 dat to be applied to sensor e at_fwhmdetect incube dbfile respfile resol outfile featureflags vis zen ele alti chlist results spline zeroborder 0 1 2 range splitband overwrite e at_smiledetect incube dbfile respfile resol outfile featureflags
268. n index lt 10 e dry vegetation or mixed vegetation soil 3 lt vegetation index lt 6 e soil vegetation index lt 3 The reflectance of off nadir water pixels criterion near infrared reflectance lt 5 is not modified Figure 10 26 Nadir normalization of an image with hot spot geometry Left reflectance image without BRDF correction Right after empirical BRDF correction 10 6 2 Empirical incidence BRDF correction in rugged terrain For many surface covers the reflectance increases with increasing solar zenith and or viewing angle 50 Scenes in mountainous regions often exihibit a large variation of terrain slopes and thus bidirectional brightness variations for a certain surface cover e g meadow or forest This behavior cannot adequately be eliminated with the Lambertian assumption of equation 10 15 This equation leads to overcorrected reflectance values in faintly illuminated areas having small values of cos Background Several approaches have been pursuit to solve this problem in the past e an empirical coefficient C is calculated based on a regression of brightness values and the local ilumination angle derived from the DEM The coefficient depends on scene content and wavelength 99 63 e the sun canopy sensor SCS geometry is employed in forested terrain instead of the solely terrain based geometry 31 e the SCS method is coupled with the C correction 97 These approaches produced
269. nd reflectance is calculated as p1 E23 P2 6 3 e decir input input This module performs the cirrus removal for the file input This means the apparent cirrus band reflectance is calculated and related to the apparent reflectance at the visible bands see 10 5 5 Finally the cirrus removed apparent reflectances are converted into the corresponding DN values e dehaze input filename fwater iw ipm ipm ihm ihm ikeep ikeep Here the filename is the name of the image level 1 data original DN stored as ENVI bsq band sequential including the path The sensor name is read from the scene inn file e g sensor worldview2 If keyword water 0 or not keyword set then only land pixels are dehazed with water 1 also water pixels will be dehazed Keyword ipm specifies the interpolation method ipm 1 is default and uses bilinear interpolation for the bright areas very fast ipm 2 uses triangulation very slow ihm 0 1 2 specifies the dehazing option ihm 1 standard dehazing ihm 2 stronger dehazing ihm 0 both options are executed the results contain the identifier dh1 and dh2 respectively Afterward the better result is renamed with dh and the inferior result is deleted if ikeep 0 or if this keyword is not specified If scene bsq is the name of the input image then the output file name is scene_dh_bilin bsq and scene_dh_trian bsq for ipm 1 2 respectively The dh indicates
270. nd water haze cloud etc is a useful product and the quality of the atmospheric correction may depend on the correct class assignment at least for some classes The previous haze cloud water land pixel classifyer is a binary decision a pixel belongs to a certain class or not In reality the decision is typically not unique and a class assignment has only a certain probability As the absolute probability of a class assignment is very difficult to assess we define three probability levels low medium and high coded 30 60 90 respectively These numbers might be interpreted as a percent probability but the numbers are relative and arbitrary Currently there are three quality layers cloud water and snow which are solely calculated with spectral criteria The quality file is written if the corresponding flag is set to 2 see chapter 9 3 and figure 5 11 in chapter 4 Cloud probability CHAPTER 10 THEORETICAL BACKGROUND 206 e low cloud probability coded 30 p blue gt 0 15 and p red gt 0 15 and p NIR p red lt 2 and p NIR gt 0 8 red and p NIR p SWIR1 gt 1 and NDSI lt 0 7 or DN blue gt Tsaturation 10 65 where p blue is the apparent reflectance in a blue band and DN blue is the corresponding digital number If no blue band is available a green band around 550 nm is taken as a substitute If no green band exists a red band around 650 nm is taken Note that saturated pixels in visible bands
271. nd water These areas are identified with spectral criteria and thresholds Default values are included in a file in the ATCOR path called preferences preference_parameters dat As an example it includes a threshold for the reflectance of water in the NIR region p 5 So a reduction of this threshold will reduce the number of pixels in the water mask A difficult problem is the distinction of water and shadow areas If water bodies are erroneously included in the shadow mask the resulting surface reflectance values will be too high Details about the processing panels can be found in section 5 4 10 2 5 BRDEF correction The reflectance of many surface covers depends on the viewing and solar illumination geometry This behavior is described by the bidirectional reflectance distribution function BRDF It can clearly be observed in scenes where the view and or sun angles vary over a large angular range Since most sensors of the satellite version of ATCOR have a small field of view these effects play a role in rugged terrain for the wide FOV sensors such as IRS 1D WiFS or MERIS and if mosaics CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 25 Figure 2 7 Zoomed view of central part of Figure 2 6 Courtesy of European Space Imaging Color coding RGB bands 4 3 2 Left original center shadow map right de shadowed image of images registered with variable observation angles are to be produced For flat terrain scenes
272. nd2 tif etc Again the maximum number of bands is restricted to 9 The first band is specified to ATCOR and all bands will be processed automatically e An optional keyword tiff2envi exists for the ATCOR batch and tiling modes that produces an automatic conversion of the input TIFF file e g image tif into the corresponding ENVI file e g image_envi bsq This file is used as a temporary input file to ATCOR and it will be automatically deleted when the ATCOR run is finished Two output surface reflectance files will be generated if tiff2envi 1 an ENVI file image envi_atm bsq and a TIFF file image_atm tif The ENVI output is needed if the spectral classification SPECL module is run otherwise it may be deleted by the user In the interactive ATCOR mode with GUI panels the default for a TIFF input file is tiff2envi 1 i e a corresponding input ENVI bsq band sequential file is created In the batch mode the default for a TIFF input file is taff2envi 0 i e the ENVI file conversion is switched off and no intermediate files are created This will save disk space and some file con version time However if the image ini file specifies the de hazing or de shadowing option then tiff2envi is reset to 1 to enable the creation of of the needed additional intermediate ENVI files For non TIFF files the tiff2envi keyword is ignored Summary of output data types e byte default surface reflectance scale fa
273. nding fractional value e an update of the path radiance in the blue to red spectral region is performed if required provided a blue spectral band exists e water vapor retrieval using the previously calculated visibility map If the scene contains no reference areas the user has to specify a constant visibility that enters the water vapor calculation e reflectance spectrum retrieval with pixel based water vapor and visibility map Iterations for adjacency effect and spherical albedo are included For the adjacency correction the reflectance of cloud pixels is replaced with the scene average reflectance to avoid an overcor rection of the adjacency effect e temperature emissivity retrieval if thermal bands exist CHAPTER 10 THEORETICAL BACKGROUND 241 10 7 2 Algorithm for rugged terrain The algorithm for rugged terrain basically consists of the same processing step as in the flat terrain but every step has to take into account some or all DEM information e During the calculation of the visibility index map the DEM information elevation slope aspect skyview factor is taken into account e The retrieval of the water vapor map has to include the terrain elevation e The empirical BRDF correction is based on the local illumination map local solar zenith angle derived from the slope aspect and shadow channels e The retrieval of the spectral reflectance cube consists of the steps three iterations for terrain re
274. nfile calfile eOsolar outfile scale zen date Apparent reflectance calculation CHAPTER 6 BATCH PROCESSING REFERENCE 132 PARAMETERS calfile ATCOR calibration file to be used for conversion of the cube eOsolar File containing the solar irradiance for the sensor atcor file outfile name of output KEYWORDS scale scale for processing same convention as for ATCOR scale 1 0 is floating point output zen solar zenith angle default 0 degrees in degrees date date as two elemt array day month e at_shadowdetect infile calfile eOsolar foutfile solangles slopefile skyview min all_layers pixfilter range Image based shadow detection classification PARAMETERS infile file to be analysed calfile calibration file for current input file eOsolar solar irradiance file for current input outfile output file of the processing KEYWORDS castshadow if keyword is set an existing cast shadow file is provided in order to find the shadows through this keyword slopefile use slope and aspect and include them in the illumination solangles solar angles zenith azimuth only required for slopefile option skyview calculate additional skyview layer based on illumination requires keyword solangles and slopefile being set range range of index default 0 5 1 0 min minimum value of output default 0 pixfilter set to filter single pixels from dark areas to avoid artifacts i
275. nisotropy map ANIF multiplicative BRDF correction corrected image spectral albedo BHR Figure 10 29 Image correction scheme The image processing procedure is following the below steps compare Figure 10 29 e calculate the BCI from image e calculate the scene specific angular kernels subsets e interpolate the calibration data from BCI levels to a continuous BRDF model CHAPTER 10 THEORETICAL BACKGROUND 238 e calculate an anisotropy map by scaling the kernels using the BCI the scan angles observation zenith and azimuth angle and the interpolated BRDF model and e apply the anisotropy map on a per pixel basis The anisotropy factor is derived as relation of the directional model for each pixel to the same model averaged over all angles i e to a good approximation of the spectral albedo BHR ANTF Piso Joss ad Tole y PBRE 10 136 Piso faecoK geo oral PBHR The bihemispherical reflectance is described by the two hemispherical averages nom and Koi weighted by the respective factors and added to the constant isotropic reflectance Piso Alter natively the anisotropy with respect to nadir BRF would be an option as done in earlier BRDF research 84 This option is currently not supported in BREFCOR as the BHR is the more generic spectral albedo definition for surface object characterization The corrected bihemispherical reflectance is finally calculated as ppyR PADRE where PHD
276. ns no thermal band s from which a map of ground temperature can be derived then Rsolar is the only surface energy component that can be evaluated In case of flat terrain with spatially varying visibility conditions or rugged terrain imagery a map of the global radiation is included as an additional value added channel 2 5 um B J E dA 7 10 0 3um For flat terrain imagery with constant atmospheric conditions the global radiation is a scalar quan tity and its value can be found in the log file accompanying each output reflectance file For rugged terrain imagery the global radiation accounts for the slope aspect orientation of a DEM surface element With thermal bands a ground temperature or at least a ground brightness temperature image can be derived Then the emitted surface radiation is calculated as O s T 7 11 where es is the surface emissivity o 5 669 x1078 Wm K is the Stefan Boltzmann constant and T is the kinetic surface temperature For sensors with a single thermal band such as Landsat TM an assumption has to be make about the surface emissivity to obtain the surface temperature Usually s is selected in the range 0 95 1 and the corresponding temperature is a brightness temperature A choice of es 0 97 or s 0 98 is often used for spectral bands in the 10 12 um region It introduces an acceptable small temperature error of about 1 2 C for surfaces in the emissivity range 0 95 1 Examples a
277. nt In addition the upper threshold defining haze or sun glint might be scene dependent However the default values usually provide good results and a solid basis for a possible iteration of these two parameters CHAPTER 10 THEORETICAL BACKGROUND 220 Figure 10 19 presents an example of haze removal over water with the two default values of T clear 0 04 and To haze 0 12 The de hazing over water is successful to a large ex tent however some artifacts appear close to the land border image center where haze pixels over water are classified as land or cloud This is due to a simple spectral classification of the land water mask an external water map would lead to better results Figure 10 19 Haze removal over water ALOS AVNIR2 true color image northern Germany 16 April 2007 Left part of original scene right after haze removal 10 5 5 Cirrus removal On the first glance images contaminated by cirrus appear similar to hazy scenes discussed in the previous section However haze usually occurs in the lower troposphere 0 3 km while cirrus clouds exist in the upper troposphere and lower stratosphere 8 16 km The effect of boundary layer haze can be observed in the visible region but seldom in longer wavelength channels gt 850 nm However cirrus also affects the NIR and SWIR spectral regions Thin cirrus clouds are difficult to detect with broad band multispectral satellite sensors in the atmospheric window regi
278. nt formats existed in the past depending on the international Landsat processing station and year of processing For this data the radiometric calibration is varying as a function of the day after launch compare references 96 41 A file cal_gain_table_kamstrup_hansen dat is available on the directory atcor cal landsat4_5 containing the gain c1 values for 1985 2005 for bands 1 4 calculated with the regression equation of 41 Other publications deviate 10 20 from the Kamstrup Hansen cl values However the historical Landsat archive was re processed with LPGS and it is recommended to use only this source 9 6 2 Landsat 8 The meta data file MTL txt is similar to the one for Landsat 7 ETM However the encoding is 12 bits pixel therefore the previous Qmax 65535 and Qmin 1 in equation 9 5 Data for the different spectral bands is delivered as TIF files one file per band Band B8 is the panchromatic band which should not be included in the multispectral MS set of OLI TIRS bands for ATCOR Band B9 is the cirrus band 1 38 wm B6 is the 1 6 um SWIR1 channel and B7 is the 2 2 um SWIR2 channel Attention the MS set of Landsat 8 bands for ATCOR should be wavelength ascending This should be performed using the function File Import Landsat 8 OLI TIRS or Landsat 8 OLI The combination OLI TIRS contains 10 bands 8 OLI bands without panchromatic and the 2 TIRS bands The output image will have
279. nterpolated to a New Reference Wavelength Grid INPUT IMAGE _atn bsq data hyperion USGS_Febli subset H PLiNov2009_167_bsq_smi_atm bsq Smile polynomial file HYerc_id l atcor atcor_23 sensor hyperion_167_smile smile_poly_ordd dat Options se 1 reference wavelength grid wavelength for center of detector array for each band se 2 reference wavelength grid wavelength average over all detector columns per band 2 3 reference wavelength grid nominal position center wavelengths of ENVI header RUM L Bass A QUIT Figure 5 63 Spectral smile interpolation This routine is used after smile dependent atmospheric correction It applies a linear interpolation on the reflectance data in order to bring the spectral bands to a common reference in across track direction The inputs are as follows see Fig 5 63 Inputs Input Image A hyperspectral image cube usually the output of atmospheric correction in smile mode _atm bsq Smile polynomial file The file smile_poly_ord4 dat as of the sensor definition used for the smile aware atmospheric correction CHAPTER 5 DESCRIPTION OF MODULES 105 Options Three options for the spectral reference wavelength grid to be used for interpolation may be selected center of detector array The spectral position of the center pixel in across track direction of the detector is taken as the reference wavelength for each spectral band average over
280. o enable an accurate interpolation ATCOR RESLUT lt gt MODTRANS gt Spectral Database gt 10 MB 0 4 nm Figure 9 1 Monochromatic atmospheric database The database comprises calculations performed for a satellite altitude of 700 km but for consis tency with the airborne ATCOR the symbolic height 99 000 is used in the file names For off nadir geometries the earth incidence angle of the satellite view is used for the MODTRAN runs to make the results independent of the satellite orbit altitude MODTRAN s mid latitude summer atmo sphere was used for the air pressure and temperature height profiles at six water vapor columns W 0 4 1 0 2 0 2 9 4 0 and 5 0 cm or or g cm sea level to space values and different aerosol types These represent dry to humid atmospheric conditions 68 8 They are needed for the water vapor retrieval to create interpolated extrapolated values for the range W 0 3 5 5 cm In spectral regions where water vapor absorbs the accuracy of the surface reflectance retrieval depends on the number of water vapor grid points and the interpolation method full range of W or sub interval pertaining to a pixel 75 The CO mixing ratio of the atmosphere is set at 400 ppmv the ozone column is fixed at 330 DU Dobson Units equivalent to the former 0 33 atm cm for a ground at sea level The aerosol types rural urban maritime and desert have been provided in the database CHAP
281. o unique relationship between the horizontal visibility and the vertical total optical thickness of the atmosphere However with the MODTRAN radiative transfer code a certain relationship has been defined between these two quantities for clear sky conditions as shown in Fig 2 1 left for a path from sea level to space The optical thickness can be defined separately for the different atmospheric constituents molecules aerosols so there is an optical thickness due 15 CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 16 to molecular Rayleigh and aerosol scattering and due to molecular absorption e g water water ozone etc The total optical thickness is the sum of the thicknesses of all individual contributors molecular scattering aerosol 6 molecular absorption 2 3 The MODTRAN visibility parameter scales the aerosol content in the boundary layer 0 2 km altitude For visibilities greater than 100 km the total optical thickness asymptotically approaches a value of about 0 17 which at 550 nm is the sum of the molecular thickness 4 0 0973 plus ozone thickness 6 0 03 plus a very small amount due to trace gases plus the contribution of residual aerosols in the higher atmosphere 2 100 km with 6 0 04 The minimum optical thickness or maximum visibility is reached if the air does not contain aerosol particles so called Rayleigh limit which corresponds to a visibility of 336 km at sea
282. ocal solar zenith angle Or greater than the scene s solar zenith angle O Equation 10 123 defines the implemented basic geometric correction function which depends on the local solar incidence angle solar illumination 6 and the threshold angle 6r The exponent b 1 3 1 2 3 4 or 1 is the second parameter and can be selected by the user Some guidelines on the choice of b are are discussed below The third adjustable parameter is the lower bound g of the correction function see Figure 10 27 G cosB cos By y gt g 10 123 The threshold illumination angle 6r should have some margin to the solar zenith angle to retain the original natural variation of pixels with illumination angles close to the solar zenith angle The threshold angle can be specified by the user and the following empirical rules are recommended e r 0 20 if 0 lt 45 e If 45 lt 0 lt 55 then Br 0s 15 e If 0 gt 55 then Br 0 10 These rules are automatically applied if 6r 0 e g during batch processing The geometric function G needs a lower bound g to prevent a too strong reduction of reflectance values Values of G greater than 1 are set to 1 and values less than the boundary g are reset to g This means the processing works in the geometric regime from r to 90 and the updated reflectance is Pg PLG 10 124 where pz is the isotropic Lambert value Figure 10 27 shows a graphical presentation of
283. ocess is then X x Y tiles ATCOR method selection between flat processing and rugged processing for the latter the DEM has to be prepared and ready After starting the process using the run button the messages are printed to the prompt or the console if available Error and status messages may also be found in the log file during and after processing 0006 IX ATCOR Tiled Processing Define Name of Output Cubet src_id1 atcor atcor_28 deno_data tn_rugged tn_bl forest atm bsq Number of Tilest K Dimension p Timensiont le Help Run Tone Figure 5 50 ATCOR Tiled Processing E 5 4 13 Landsat 8 TIRS Calculate Temperature Use this function to calculate the surface temperature from Landsat 8 TIRS data using an advanced dual band split window technique CHAPTER 5 DESCRIPTION OF MODULES Figure 5 51 TIRS module 93 CHAPTER 5 DESCRIPTION OF MODULES 94 5 5 Menu BRDF The BRDF menu provides access to the simple nadir normalization method and the more ad vanced BREFCOR BRDF correction w Satellite AT C OR File Sensor Topographic ATCOR ERDF Filter Simulation Tools BREFCOR Correction Licensed for Daniel 1 DLR ReSe 2015 Nadir Normalization Wide FOV Imagery Mosaicking Figure 5 52 Filter modules 5 5 1 BREFCOR Correction This module calculates an observer BRDF correction using a model based approach see chapters 2 5 10 6 3 Figure 5 53 show
284. oefficients and vegetation indices studied by model simulations Remote Sensing of Environment Vol 50 1 17 1994 15 Ll Coll C Caselles V Rubio E Sospreda F and Valor E Temperature and emissivity separation from calibrated data of the Digital Airborne Imaging Spectrometer Remote Sens Environm Vol 76 250 259 2001 16 Coll C Richter R Sobrino J A Nerry F Caselles V Jimenez J C Labed Nachbrand J Rubio E Soria G and Valor E A comparison of methods for surface temperature and emissivity estimation In Digital Airborne Spectrometer Experiment ESA SP 499 p 217 223 Nordwijk Netherlands 2001 17 Dell Corripio J G Vectorial algebra algorithms for calculating terrain parameters from DEMs and the position of the sun for solar radiation modelling in mountainous terrain Int J of Geographical Information Science Vol 17 1 23 2003 Crist E P and Cicone R C A physically based transformation of Thematic Mapper data the Tasseled Cap IEEE Trans Geosci Remote Sensing Vol GE 22 256 263 1984 18 Le 19 LL Dozier J Bruno J and Downey P A faster solution to the horizon problem Computers amp Geosciences Vol 7 145 151 1981 20 L Dell Endice F Nieke J Schl pfer D and Itten K I Scene based method for spatial misregistration detection in hyperspectral imagery Applied Optic
285. of shadow Theoretically it can be zero i e a completely shadowed pixel receiving only diffuse solar illumination However a too low estimate close to zero will boost the surface reflectance especially for channels in the 1 5 2 5 um region eq 10 118 since the diffuse solar radiation term Kaif 18 very small Therefore small positive values of Phin are recommended The range of Phin is typically from 0 05 to 0 1 with the default set at P7 0 08 The third tunable parameter is Pmax providing the range of stretching of the unscaled shadow function into the scaled function The default of Par is the location of the maximum of the histogram of but it could be set at a greater value if the corrected image is too dark in the expanded shadow regions which indicates the histogram maximum does not represent fully illuminated areas The advantage of the presented method is its fast processing performance because it relies exclu sively on spectral calculations and avoids time consuming geometric cloud shadow pattern consid erations The drawback is that useful geometric information is neglected In some cases it is useful to have the de shadowed digital number DN image in addition to the surface reflectance product This facilitates a comparison with the originally recorded DN imagery The conversion from reflectance to the corresponding at sensor radiance is performed with eq 10 1 Then eq 10 6 is employed to compute the de
286. olds e The mask pixels have to fulfill rvi gt 3 and Pnir gt 0 10 and Pnir lt 0 25 and Preg lt 0 04 Water pixels are automatically excluded from this mask because of the pnir gt 0 10 condition and soil pixels are excluded with the combination of all four conditions If the percentage of reference pixels is smaller than 2 of the scene the search is iterated with VIS 60 km covering the very clear conditions of visibility 40 60 km Again if the percentage is smaller than 2 the search is iterated with VIS 10 km to cover higher aerosol loadings VIS 8 15 km Each visibility iteration is supplemented with an iteration of the threshold prea which is decreased in steps of 0 005 down to Preg 0 025 to include only the darkest vegetation pixels see 73 for details Currently the algorithm terminates if less than 2 reference pixels are found after these two iterations In this case the user has to employ the constant visibility option specifying the value of the visibility for the scene During batch mode operation the program takes the specified visibility from the inn file Then a check for negative reflectance pixels is performed with dark pixels in the red band 660 nm vegetation and the NIR band 850 nm water and the visibility is iteratively increased up to VIS 60 km to reduce the percentage of negative reflectance pixels below 1 of the scene pixels A corresponding notice is given in the atm log output
287. olumn and reflectance in CHAPTER 5 DESCRIPTION OF MODULES 82 the second column The resampling can then be done with the sequence Filter Resample a Spectrum from the ATCOR main panel The result is an ASCII file with 2 columns the first contains the channel center wavelength the nm and wm unit is allowed the second contains the resampled reflectance value either in the 0 1 or 0 100 range If target1 is the name of the target ATCOR provides three ASCII files with information on target background properties and the derived calibration file These are the output of the c1 option of ATCOR s calibration module e File targetl adj contains the original target DN the adjacency corrected DNf and the ground reflectance data for each band e File targetl rdn radiance versus digital number contains the band center wavelength target radiance L and corresponding digital number This file can be used as input to a regression program cal_regress that allows the calculation of the calibration coefficients with a least squares fit in case of multiple targets more than two e File targetl cal contains three columns band center wavelength offset or bias set to zero co 0 and c according to equation 2 13 Remark a bright target should be used here because for a dark target any error in the ground reflectance data will have a large impact on the accuracy of c1 Hod
288. on The first step is the search for dark pixels using a small local nonoverlapping window box w x 3 pixels for the calculation of the haze thickness map HTM For this purpopse a blue spectral channel is employed because it is most sensitive to haze If no blue band exists the green band is taken The next step calculates an additional HTM map with a moderately large window size e g w 21 x 21 pixels It is used to label haze and haze free regions by thresholding this HTM map The third step calculates the correlation of the band specific HTM A maps and re scales these maps in the inteval 1 0 for A Abiue 2 2m More details can be found in the reference paper 56 10 5 3 Haze removal method 2 The method 2 haze removal algorithm runs fully automatic It is a combination of the improved methods 66 105 and consists of five major steps 1 Masking of clear and hazy areas with the tasseled cap haze transformation 18 TC x x BLUE x x RED 10 96 where BLUE RED x1 and z2 are the blue band red band and weighting coefficients respectively The clear area pixels are taken as those pixels where TC is less than the mean value of TC 2 Calculation of the regression between the blue and red band for clear areas clear line slope angle a see figure 10 17 If no blue band exists but a green spectral band then the green band is used as a substitute 3 Haze areas are orthogonal to the clear line
289. on If in doubt the rural continental aerosol is generally a good choice The aerosol type also determines the wavelength behavior of the path radiance Of course nature can produce any transitions or mixtures of these basic four types However ATCOR is able to adapt the wavelength course of the path radiance to the current situation provided spectral bands exist in the blue to red region and the scene contains reference areas of known reflectance behavior The interested reader may read chapter 10 4 2 for details Visibility estimation Two options are available in ATCOR e An interactive estimation in the SPECTRA module compare chapter 5 The spectra of different targets in the scene can be displayed as a function of visibility A comparison with reference spectra from libraries determines the visibility In addition dark targets like vegetation in the blue to red spectrum or water in the red to NIR can be used to estimate the visibility e An automatic calculation of the visibility can be performed if the scene contains dark reference pixels The interested reader is referred to chapter 10 4 2 for details CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 20 Water vapor column The water vapor content can be automatically computed if the sensor has spectral bands in water vapor regions e g 920 960 nm The approach is based on the differential absorption method and employs bands in absorption regions and window regions to
290. on in addition to using float data before resampling it is recommended to calcu late the slope aspect maps on the original coarse spatial resolution data followed by the high resolution resampling step for all DEM files elevation slope aspect Do not employ the sequence of resampling the elevation followed by a slope aspect calculation of the high resolution elevation map because this approach enhances artifacts Steps to reduce slope aspect striping FLOAT ELEVATION file if it is stored as integer Calculate SLOPE and also ASPECT with a low pass filter of 5x5 pixels Resize SLOPE ASPECT file factor 4 larger Apply a lowpass filter 7x7 pixels ao A WH NR Resize with factor 0 25 using nearest neigbor to obtain the original size Note that the function DEM Smoothing chap 5 3 7 allows an efficient DEM smoothing e Landsat and ASTER thermal band processing The thermal band s are sensitive to the atmospheric water vapor column However the thermal band atmospheric LUTs US standard mid latitude summer tropical etc only pro vide a discrete set of water vapor columns u see chapter A If the nearest u value from this set deviates more than 0 2 0 3 cm from measured data e g radiosonde profile the user may generate a new thermal LUT with a water vapor column u as a linear combination of two existing LUTS with water vapor contents uy and ug u wiu 1 w1 uz Example u 2 4 cm uy 2 08 cm s
291. on and ground reflected radiation The scattered radiation component is also called path radiance It depends on the solar and viewing geometry as sketched in Fig 10 3 In case of a flat terrain the at sensor radiance L can be written as Asrar 1989 chapter 9 E L L y 5 4 ne 10 1 CHAPTER 10 THEORETICAL BACKGROUND 186 Figure 10 3 Radiation components illumination and viewing geometry L at sensor radiance for surface reflectance p Lp path radiance Ta total ground to sensor atmospheric transmittance sum of direct Tqir and diffuse Tq f transmittance E global flux on a horizontal surface sum of direct Eqir and diffuse Haz flux Eg 0 is calculated for a ground surface with p 0 Dr large scale reference background reflectance determining the effective global flux p 0 15 is used for ATCOR S spherical albedo of the atmosphere accounts for atmospheric backscattering to the ground The geometry is described by the angles view zenith and solar zenith and relative azimuth angles compare figure 10 3 Since p and p are not known for image data and can vary within a scene equation 10 1 has to be solved for p iteratively compare equations 10 9 10 15 In a strict sense the reflectance p used here should be called hemispherical directional reflectance factor HDRF because most surfaces show an anisotropic reflectance behavior characterized by the bidirectional reflectance dist
292. on the IDL command line Then continue with gt atcor2_batch input datal examples example_image bsq case of flat terrain or gt atcor3_batch input datal examples example_image bsq case of rugged terrain y At this stage all required input parameters are already available in the inn file in this specific case erample_image inn The submitted job is a quasi batch job the corresponding IDL window is used for error and status messages and it may not be closed during the run time of the job A log file is created during processing e g erample_tmage_atm log which contains information about the job status It contains three message levels I Info W Warning E Error followed by a two digit number between 0 and 99 and a space e g W19 followed by the appropriate information These three message levels can easily be parsed by a separate user program if desired Other information in the log file is marked with the hashmark symbol in the first column In the tiling mode the user has to specify the number of tiles in x column direction ntx and in y line direction nty e g gt atcor2_tile input datal examples example image bsq ntx 3 nty 2 CHAPTER 6 BATCH PROCESSING REFERENCE 125 In this case the image is split into 3 2 6 tiles each tile is processed separately finally all tiles are merged into one output file and the sub scenes are deleted The maximum size of each tile
293. ons espe cially over land because land scenes are spatially inhomogeneous and this type of cloud is partially transparent On the other hand water vapor dominates in the lower troposphere and usually 90 or more of the atmospheric water vapor column is located in the 0 5 km altitude layer Therefore if a narrow spectral band is selected in a spectral region of very strong water vapor absorption e g around 1 38 um or 1 88 um the ground reflected signal will be totally absorbed but the scattered cirrus signal will be received at a satellite sensor or a sensor in a high altitude aircraft e g 20 km AVIRIS scenes So a narrow channel at 1 38 um is able to detect cirrus clouds and if a correlation of the cirrus CHAPTER 10 THEORETICAL BACKGROUND 221 signal at this wavelength and other wavelengths in the VNIR and SWIR region can be found then the cirrus contribution can be removed from the radiance signal to obtain a cirrus corrected scene The basic ideas of cirrus correction were presented in several papers 25 26 28 80 The algorithm differs for water and land pixels For water a scatterplot of the 1 38 um versus the 1 24 yum channel is used for land the band correlation is determined from a scatterplot of the 1 38 wm versus a red channel around 0 66 wm To obtain a high sensitivity only vegetation pixels are taken because they have a low reflectance in the red spectral region so the cirrus contribution is easily tr
294. ook up table LUT results of the radiative transfer calculations separately for the solar and thermal region These LUTs are calculated for a nadir view but for tilt sensors the files phasefct bin in the atcor bin directory contain the path radiance as a function of the scattering angle and the appropriate file is automatically included INPUT IMAGE FILE Yexport datadatar atcor2 3 demo_data tm_flat tm_essen1000 bsq Date dd mme year 2008 1989 OUTPUT IMAGE FILE Fexport data data atcor2 3 demo_data tm_flat tm_essenLOO0_atm bsq OVERWRITE Scale Factor 4 0 Help PREi by ier d api Deomeipa Band selection Selected SENSOR Landsat 4 5 TH Select MH Z Pixel size m 80 0 CALIBRATION FILE Yexport datadatar atcor 3 cal landsatd_5 tm_standard cal ATMOSPHERIC FILE aansrura ATH FILE for thermal bandis midlat_summer Adjacency range km 1 00 Help Zones Hl Visibility km 19 1 Solar zenith degree 343 0 Ground elevation km 30 1 SPECTRA AEROSOL TYPE VISIE ESTIMATE INFLIGHT CALIBRATION Help WATER VAPOR IMAGE PROCESSING Output file already exists change name or press OVERURITE MESSAGES QUIT Figure 4 6 ATCOR panel for flat terrain imagery Note that for a new user specified sensor these LU T s have to be calculated once prior to the first call of ATCOR This is done with the module RESLUT see section 5 2 5 available under the Sensor menu It is recommended to check the qualit
295. ool is an optional add on to the satellite ATCOR Although it is mainly intended for hyperspectral sensors it can of course also be employed for user defined multispectral instruments The first step is the definition of a new sensor subdirectory compare Figure 4 13 and Fig 5 13 It serves as the start folder of further subdirectories for the user defined hyperspectral sensors e g chris_ m1 A few sensor description files have to be created by the user as explained in the next section atcor bin cal sensor chris_m1 chris_m3 atm_ database atm_database_chris atm_lib standard multispectral sensors chris_m1 chris_m3 spec_lib Figure 4 13 Directory structure of ATCOR with hyperspectral add on CHAPTER 4 WORKFLOW 40 4 6 1 Definition of a new sensor A few steps have to be taken to include a new satellite sensor in ATCOR They are supported by the respective panels On the level of the ATCOR installation directory the following steps are to be taken 22 e A new sensor subdirectory in the atcor sensor folder has to be created This is easiest done using the routine Define Sensor Parameters as described in chapter 5 2 1 Please make sure this name does not agree with an existing multispectral sensor name in the atcor cal directory This process may also be done manually by copying and adapting an existing user defined sensor or one of the samples provided or use the funct
296. oot geometry across track angular saepling interval 1 degree no hot spot geometryt scroms Erack angular sampling interval 3 degree RUN QUIET Figure 5 54 Nadir normalization CHAPTER 5 DESCRIPTION OF MODULES 97 5 5 3 Mosaicking Mosaic a number of georectified scenes into one in an efficient way Figure 5 55 shows the parameter settings which can be chosen for this procedure Inputs Input Files first file on top list of files to be mosaicked The files are stacked in the order of appearance i e the first file in the list is on top of the mosaicked output Buttons Add File s adds one or more new files Remove Entry removes the selected file s emphMove moves the file one position up or rotates if already at top Range range in x and y direction to edge of pixels of the mosaicked product should be entered The coordinates refer to the pixel borders edges on either side of the image Button From Files reads the maximum extend of all selected files as of the list Pixel Size Size of output pixels in meters Note the mosaicking uses a bilinear interpolation no aggregation is done Cut Options treatment of image borders in overlap area Edge Overlay The mosaicikng is such that the first file is in the list is strictly on top Center Cropped While mosaicking the routine tries to find the middle of the overlap area between the new image and all the images mosaicked so far as a cut line
297. ooth the outputs by a lowpass filter of size smooth after derivative filtering adj use only adjacent bands excluding current fro derivatives e at_pushpoli infile outfile gainfile spatial nospectral zero_offset Spectral polishing and post calibration destriping on the basis of pixel wise deviations from local average PARAMETERS infile file of reflectances to be filtered outfile name of output file to be created gainfile optional output file containing gains and offsets will be stored KEYWORDS spatial use the spatial dimensions with a filter size of the keyword for filtering nospectral don t apply spectral interpolation by default spectral interpolation with 3 bands is used to get systematic offsets zero_offset apply linear function through zero no offset e at_prepele infile demfile ofile kernelsize fillzero log slopasp skyview Program to resize and prepare a DEM and all its layers for ATCOR processing PARAMETERS infile ENVI formatted BSQ file to be processed with ATCOR demfile ENVI DEM covering the area of ifile same coordinate system can be different resolution extent ofile name of primary elevation file KEYWORDS kernelsize size of kernel to calculate slope aspect default 5 fillzero fill in zero values at edges of image shrink image slopasp set to calculate slope aspect files skyview set to calculate skyview factor file e at_rhoapp i
298. or see below e date of the year given exactly as day month eg 26 7 for July 26th used for sun earth distance calculation e solar zenith angle use Tools Solar Zenith and Azimuth for its calculation Output A cube containing the scaled apparent reflectance in is stored The data format is driven by the scaling factor as follows e scale lt 10 byte e scale gt 10 integer e scale gt 500 unsigned integer e scale lt 1 floating point effective value unit wavelength reference and FWHM are taken from the file e0_solar_x spc Ar A ATCOR Apparent Reflectance Calculation elect Input File Name data nyper ion Bern_02 Hyper ion_subt67 bsq elect Calibration Filet sro_idl atcor atcor_28 sensor hyper iont67 hyper ion 167 cal elect Solar Reference File EQ sro_id atcor atcor_22 sensor hyper iont67 e0_solar_hyper iont67 spc efine Name of Output Cube data hyperion Bern_02 Hyperion_sub_rhoapp b q Scale factor x Refl E Date dem Eve Solar Zenith deg E Help Done y Es ELE LE E Figure 5 66 Apparent Reflectance Calculation 5 7 4 Resample Image Cube This routine allows to simulate a multispectral image from imaging spectroscopy data A detailed description of this routine is given in chapter 8 CHAPTER 5 DESCRIPTION OF MODULES 109 5 8 Menu Tools The Tools menu contains contains a collection of useful routines such as the calcula
299. or the retrieval of a water vapor map without a calculation of the surface reflectance cube Th button IMAGE PROCESSING starts the atmospheric correction process from the entered parameters A series of sequential panels are displayed after this button is pressed Fig 4 7 shows the panel with the image processing options Some options may not be accessible they are blocked if the required spectral bands are missing In case of a mountainous terrain the ATCOR3 button has to be selected Fig 4 1 ATCOR This panel is similar to Fig 4 6 but an additional panel for the specification of the DEM files will appear Fig 4 8 The user has to provide the DEM file matched to the size of the input image The slope and aspect files can be calculated from the corresponding module under Topographic Fig 4 1 These two files may need a special treatment as discussed in chapter 5 4 11 Therefore they are not automatically created from the elevation file The skyview file and cast shadow file are optional only required in extremely steep terrain The skyview calculation can also be found under the Topographic label of Fig 4 1 Blocked Options Are Not Available For The Selected Sensor Might also apply for a reduced set of bands Either Haze or Cirrus Removal not both Blocked Options Are Not Available For The Selected Sensor Variable Visibility aerosol optical thickness Yes No Might also apply for
300. orrection function e Gain only constrain the residual gain to an offset of 0 this is the typical situation Output A cube containing the spectrally filtered copy of the original image data cube is created 5 6 6 Pushbroom Polishing Destriping This routine treats each detector pixel of a pushbroom imaging spectrometer separately and derives gain and optional offset values in comparison to its direct neighbors The routine may be used for both spectral polishing of residual gain offset errors and for destriping of pushbroom imagery Inputs Input file name A hyperspectral image cube usually the output of atmospheric correction _atm bsq Interpolation Distance Number of pixels from center pixel i e a factor of 2 uses 2 pixels on each side for calculation of residual gains The distance should be in a range of the width of visible striping artefacts CHAPTER 5 DESCRIPTION OF MODULES 103 AAA ATCOR Pushbroom Radiometric Polishing select Input File Name Yhyspex biol_2_VNIR_1600_SN0004_7940_us_2x_2007 07 16T095204_rad_atm bsq Interpolation Distance in Spatial Dimension pixels E Polishing Filter Type w Spectral w Spatial 2D Filter Type of Correction Function wv Gain and Offset Gain only Def in Polished Output Data Cube hyspex biol_2_VNIR_1600_SN0004_7940_us_2x_2007 07 167095204_rad_atm_polish bsq Run Polishing Done y A Figure 5 62 Pushbroom radiometric polishing Polishing Filter T
301. oss track FOV degree pixels per line first last reflective band 0 35 2 55 um first last mid IR band 2 6 7 0 wm first last thermal band 7 0 14 um no tilt in flight direction required dummy 1 smile sensor 5 Gaussian spectral channel filter Table 4 3 Sensor definition file smile sensor without thermal bands channel number or wavelength the remaining columns contain the polynomial coefficients starting with bo Imagery from smile sensors must be processed in the raw geometry IGM Image Geometry Map to preserve the original image columns During the surface reflectance retrieval the atmospheric topographic correction is performed on a per column basis i e to each image column its appropriate center wavelength bandwidth is associated The per column processing typically implies a factor of 8 increase in processing time T he following steps are to be performed 1 Define a sensor wvl cal rsp files RESLUT using the original set of wavelengths pre launch values as provided with the data from the data provider 2 Run the smile detection tool compare Section 5 8 5 using the sensor defined in 1 and ap propriate absorption features to derive the polynomial coefficients smile_poly_ord4 dat for smile correction in step 3 alternatively enter the smile polynomial factors from laboratory calibration Note if two detectors are in the sensor system this should be done separately for VNIR and SWIR opt
302. ound If the reflectance of dark areas is known the visibility can actually be calculated The interested reader may move to chapter 10 4 2 but this is not necessary to understand the remaining part of the chapter e If the main atmospheric parameters aerosol type or scattering behavior visibility or optical thickness and water vapor column and the reflectance of two reference surfaces are measured CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 19 the quantities Lpath T p and Ey are known So an inflight calibration can be performed to determine or update the knowledge of the two unknown calibration coefficients co k c1 k for each spectral band k see section 2 3 Selection of atmospheric parameters The optical properties of some air constituents are accurately known e g the molecular or Rayleigh scattering caused by nitrogen and oxygen molecules Since the mixing ratio of nitrogen and oxygen is constant the contribution can be calculated as soon as the pressure level or ground elevation is specified Other constituents vary slowly in time e g the CO concentration ATCOR calcu lations were performed for a CO2 concentration of 400 ppmv 2015 release Later releases might update the concentration if necessary Ozone may also vary in space and time Since ozone usually has only a small influence ATCOR employs a fixed value of 331 DU Dobson units correspond ing to the former unit 0 331 atm cm for a ground at sea level r
303. ound level is typically 80 or more of the total downwelling flux Channels in the blue to red region 0 4 0 7 um are not used for the detection of shadow regions because they receive a much larger diffuse radiation component making them less CHAPTER 10 THEORETICAL BACKGROUND 224 sensitive to partial shadow effects Instead visible channels serve to define a potential cloud mask The surface reflectance is first computed with the assumption of full solar illumination i e the global flux on the ground consists of the direct Eqir and diffuse Kaz component If DN denotes the digital number of a pixel Lp the path radiance and 7 the atmospheric transmittance ground to sensor the surface reflectance can be obtained as _ m d eo t cli DN x y Lpi pila y A ee ep 10 112 Ti Eiri Laas Here d is the Earth Sun distance at the image acquisition time in astronomical units cy and cj are the radiometric calibration coefficients offset and slope to convert the digital number into the corresponding at sensor radiance L i e L co c DN and iis the channel index The proposed de shadowing algorithm consists of a sequence of eight processing steps as sketched in Fig 10 22 It starts with the atmospheric correction The next step is the masking of water bodies and cloud areas with simple spectral criteria as detailed below Water pixels have to be excluded as far as possible to avoid their assignment as shadow pixels
304. oved aerosol retrievals using the ratio of 1 38 um and 1 24 um channels Geophys Res Letters Vol 29 No 18 1890 36 1 to 36 4 2002 References 245 28 Gao B C Meyer K and Yang P A new concept on remote sensing of cirrus optical depth and effective ice particle size using strong water vapor absorption channels near 1 38 and 1 88 um IEEE Trans Geosci Remote Sens Vol 42 1891 1899 2004 29 L Gillespie A R Lithologic mapping of silicate rocks using TIMS In Proc TIMS Data User s Workshop JPL Publ 83 38 Pasadena CA pp 29 44 1986 30 LA Gillespie A et al A temperature and emissivity separation algorithm for Advanced Space borne Thermal Emission and Reflection Radiometer ASTER images IEEE Trans Geosc Remote Sensing Vol 36 1113 1126 1998 31 Gu D and Gillespie A Topographic normalization of Landsat TM images of forest based on subpixel sun canopy sensor geometry Remote Sensing of Environment Vol 64 166 175 1998 32 Guanter L Richter R and Moreno J Spectral calibration of hyperspectral imagery using atmospheric absorption features Applied Optics Vol 45 2360 2370 2006 33 Guanter L Richter R and Kaufmann H On the application of the MODTRAN4 atmo spheric radiative transfer code to optical remote sensing accepted for publication Int J Remote Sensing 30 6 14071424 doi 10 1080 01431
305. overcorrection of faintly illuminated areas where local solar zenith angles 6 range from 60 90 These areas appear very bright see Figure 2 9 left part Figure 2 9 BRDF correction in rugged terrain imagery Left image without BRDF correction Center after BRDF correction with threshold angle Br 65 Right illumination map cosp To avoid a misclassification of these bright areas the reflectance values have to be reduced Fig 2 9 center part In ATCOR empirical geometry dependent functions are used for this purpose In the simplest cases the empirical BRDF correction employs only the local solar zenith angle 6 and a threshold Pr to reduce the overcorrected surface reflectance pr with a factor depending on the incidence angle For details the interested reader is referred to section 10 6 2 The third method available in ATCOR is the BRDF effects correction BREFCOR method which uses both the scene illumination and per pixel observation angle It may also be used if a number of satellite scenes are to be mosaicked It follows a novel scheme based on a fuzzy surface classification and uses BRDF models for the correction The process follows the below steps 1 perform a fuzzy BRDF Class Index BCI image classification 2 calibrate the BRDF model using a number of scenes e g meant for mosaicing 3 calculate the anisotropy index for each spectral band using the calibrated model and the BCI 4 correct the image using the
306. ow water vapor values This water vapor threshold can be set by the user see chapter 9 3 The file xxx_out_hcw bsq haze cloud water corresponding to a scene xxr bsq contains three relative levels of cirrus optical thickness thin medium and high The corresponding thresholds are arbitrarily set depending on the statistics mean standard deviation of the apparent reflectance p2 1 38um map The file xxx_out_hcw bsq is intended as a visual aid or quicklook therefore the cirrus level maps of different scenes cannot be compared quantitatively As an example a certain scene setting could be e thin cirrus thickness color coded as light yellow with 0 010 lt p cirrus lt 0 015 e medium thickness color coded as darker yellow with 0 015 lt p cirrus lt 0 025 e high thickness color coded as bright yellow with p cirrus gt 0 025 reflectance units In addition to the 1 38 um cirrus channel another channel index w1 around 1 24 wm or as a substitute a NIR channel from the 800 to 900 nm region is employed with a ratio criterion to define cirrus pixels p cirrus p w1 gt T cir 10 111 Reference 27 proposes a threshold of T cir 0 3 to distinguish tropospheric aerosols due to dust storms from cirrus clouds However in the absence of dust storms this threshold is too high and predicted no cirrus in a number of test scenes containing a lot of cirrus clouds Therefore we use much lower values of T cir ranging
307. patially varying visibility option was selected and if the sensor has a 2 2 um or 1 6 ym band or at least a red and NIR band required for the automatic masking of the dark reference pixels compare chapter 10 4 2 Dark pixels in the 2 216 um band are determined with reflectance lt 58 DOY Reflectance ratio refl 0 661 um refl 2 216 um 1 5000 DDY Reflectance ratio ref1 0 486 um refl 0 661 um B 5000 blue red Positive ratio bluerred allows adaptation of aerosol type to DIM ratio Negative ratio bluefred aerosol type is fixed as specified Visibility Index optical thickness map is calculated based on reference pixels Options to fill the gaps in the visibility index map file _visindex b q Wo interpolation average vis index is put into gaps fast recommended se Triangular interpolation slower Tone Message Figure 5 44 Reflectance ratio panel for dark reference pixels CHAPTER 5 DESCRIPTION OF MODULES 89 The panel of figure 5 45 pops up for rugged terrain and contains the input parameters to incidence BRDF compensation due to terrain variations as discussed in chapter 2 5 we No BROF correction se CIA Empirical ERDF correction independent of surface cover 2 11 Empirical ERDF correction different for vegetation and non vegetation i local solar illumination angle t illumination angle where PRDF starts i gt solar zenith angle of scene G G1 costi Y cosit
308. pectral resolution used for correlation analysis results will be resolved by this resolution Search range maximum total spectral range used for smile detection ie 20 nm is 10 nm search distance For FWHM detection it is the maximum factor to be applied A factor of 2 e g means to search between FWHM scaling from 0 5 to 2 Band range bands which shall be used for smile detection and for which the coefficients are written to the output file Split band First band of second detector for sensor having more than one detector starting numbering at 1 NOTE for imagers with more than two detector it is recommended to derive the smile separately for each detector to avoid artefacts in the transition range Visibility horizontal visibility as of modtran conventions km Solar Zenith angle measured from zenith to sun Mean Ground Elevation Ground altitude in km Flight Altitude in km a s l Feature wavelength 17 selectable features used for smile detection the per band smile is inter polated from these feature wavelengths Interpolation type interpolation used to expand the feature wavelength results to all spectral bands Extrapolation type specifies how the bands outside of the selected features are treated Repeat repeats the last value toward the borders to zero gradually decrease to zero at border of detector CHAPTER 5 DESCRIPTION OF MODULES 115 Actions Detect Smile The module will perform the smil
309. pixels The current implementation provides a mask for haze over land see the _out_hcw bsq file The haze over water mask is treated in the next section Figure 10 18 shows an example of a subset of an Ikonos scene of Dresden where the haze removal algorithm was applied More images with the results of the haze removal method are shown on ATCOR s web page http www rese ch 10 5 4 Haze or sun glint removal over water The haze removal over water uses a near infrared NIR band to estimate the spatial distribution of haze The principal method is described in 48 We use a modified version of this approach without an interactive definition of haze polygons First the water pixels are masked either using spectral criteria or taking an external water map If the scene is named scene1 bsq the external map must CHAPTER 10 THEORETICAL BACKGROUND 219 E E a pt eE me Pla k St ine Figure 10 18 Subset of Ikonos image of Dresden 18 August 2002 Space Imaging Europe 2002 Left original scene right after haze removal Color coding RGB 4 2 1 NIR Green Blue bands be named scene1_water_map bsq a 1 channel 8 bit pixel or 16 bit pixel file where water is coded with an arbitrary positive number The external map is automatically taken if it is placed in the same folder as the scene The second step is the definition of clear water pixels using the apparent reflectance in the NIR band Pixels are la
310. pre defined multispectral sensors can also be read from the ATCOR main panel File Read Sensor Meta Data which creates the corresponding cal and inn files using some default atmospheric parameters for the latter file and setting the average scene elevation at sea level These can later be updated in the corresponding widgets of the ATCOR panel The meta file reader can also be invoked as a batch job see chapter 6 4 9 6 1 Landsat 7 ETM Landsat 5 TM The usual source of Landsat data is a download from the USGS website glovis usgs gov A scene should be downloaded with its meta file MTL txt In former years there was the NLABS pro cessing system which was replaced with the LPGS processing starting on December 8 2008 see the comprehensive article 12 The meta file reader of ATCOR only supports LPGS processed data The meta file contains the min max radiance for each band e g Lmax_band1 191 6 Lmin_band1 6 2 and the corresponding max min digital numbers Qmax Qmin The general equations to convert the digital number DN into an at sensor radiance are L B G DN 9 4 B Lmin Lmin Qmin 9 5 Qmax Qmin Lmax Lmin G 9 6 Qmaz Qmin where B bias G gain and Qmin 1 Qmax 255 for the LPGS processing the former NLAPS used Qmin 0 Qmax 255 The radiance unit in the meta file is Wm sr t umt Since ATCOR employs the unit mWem sr yum the meta file
311. quator negative is South of Equator Longitude 0 to 360 is East of Greenwhich negative West of Gr Longit 90 equivalent to Longit 270 RESULTS Solar Azimuth 171 3 Solar Elevation 65 10 Solar Zenith 24 90 degree Solar Azimuth North 0 East 90 Solar Elevation sunrise 0 Solar Zenith sunrise 90 Day of Year 170 Error message fi D0 Calculation QUIT Figure 5 68 Calculation of sun angles CHAPTER 5 DESCRIPTION OF MODULES 110 5 8 2 Classification of Surface Reflectance Signatures The spectral classification SPECL is a hierarchical classifier based on surface reflectance data employing the Landsat Thematic Mapper TM wavelengths It is not a land use classification but a classification into certain spectral categories e g dark and bright bare soil and different vegetation classes see figure 5 69 The following set of rules is used where bl b2 b3 b4 b5 and b7 indicate the surface reflectance in the TM bands 0 48 0 56 0 66 0 84 1 65 2 2 um respectively or the nearest corresponding channel snow b4 b3 lt 1 3 and b3 gt 0 2 and b5 lt 0 12 e cloud b4 gt 0 25 and 0 85 lt b1 b4 lt 1 15 and b4 b5 gt 0 9 and b5 gt 0 2 e bright bare soil b4 gt 0 15 and 1 3 lt b4 b3 lt 3 0 e dark bare soil b4 gt 0 15 and 1 3 lt b4 b3 lt 3 0 and b2 lt 0 10 e average vegetation b4 b3 gt 3 0 and 02 03 gt 0 8 or b3 lt 0 15 and 0 28 lt b4 lt 0 45 e brig
312. r filename should have the last four characters as _ele and the bsq extension The output file replaces the ending _ele with the ending _shd e g example_DEM25m_zen31_azil61_shd bsq The rounded zenith and azimuth angles will be included in the shd file name Note The shadow and skyview calculations can be omitted in gently undulated terrain Example for maximum slopes of 25 and a solar zenith angle of 40 no DEM shadow is possible Also the local trigonometric y sky view factor employed if the _sky bsq file is missing is sufficiently accurate compare figure 10 5 A TIFF input elevation file is converted into the corresponding ENVI elevation file e atcor2_batch input filename output file vis vis tiff2envi tiff2envi or atcor2_tile input filename ntr 3 nty 2 output file vis vis tiff2envi tiff2envi The 2 in atcor2_batch means the code for flat terrain i e no DEM is employed The file name must be fully qualified i e it includes the path e g data2 project1 imagel bsq The file should have the band sequential BSQ file structure A corresponding inn file e g data2 project1 imagel ini must be available that contains all processing parameters This file will be generated during the interactive session It may be also be created by the user e g employing the program write_atcor3_inn_file pro that is available on request T
313. r 32 bit integer or float data Input DEM FILE Ysre_idl atcor atcor_23 deno_data tn rugged tn_blforest_ele bsq QUIT SLOPE File Vsre_idl atcor atcor_23 deno_data tn_rugged tn_blforest_sIp bsq OVERWRITE ASPECT File Yaro_idl atcor atcor_23 deno_data tn_rugged tn_blForest _asp bsq Kernel size box for averaging E DEM resolution x y pixel size meters 30 0 DEM height z unit Sn xs dm yom MESSAGES Figure 5 23 Slope Aspect Calculation panel 4 Kernel Size Box Size of the kernel in number of pixels the slope and aspect is calculated as gradient of the pixels at the edges of this box the default value is 3 pixels i e direct neighbors of center pixel DEM resolution This is the pixel size a default of 30m is assumed This needs to be entered manually DEM height unit The unit of the values in the DEM usually a DEM is stored in meters but sometimes an integer DEM is stored as dm or cm data in order to preserve disk space Outputs The two files of slope and aspect are created same size as DEM integer data 5 3 4 Skyview Factor The sky view factor of a DEM is calculated with a ray tracing program and ranges from vsky O to 1 with 1 indicating a full hemispherical view Data in the sky view file are scaled from 0 to 100 and coded as byte The sky view factor determines the fraction of the hemispherical diffuse sky flux and 1 vsky x y determines the fraction of radiation reflected
314. r infrared are required to derive the albedo product Wavelength gap regions are supplemented with interpolation The contribution of the 2 5 3 0 um spectral region can be neglected since the atmosphere is almost completely opaque and absorbs all solar radiation The output flx file contains the channels SAVI LAI FPAR and albedo coded as 16 bit integer with the following scale factors e SAVI range 0 1000 scale factor 1000 e g scaled SAVI 500 corresponds to SAVI 0 5 e LAI range 0 10 000 scale factor 1000 e g scaled LAI 5000 corresponds to LAI 5 0 e FPAR range 0 1000 scale factor 1000 e g scaled FPAR 500 corresponds to FPAR 0 5 e Albedo range 0 1000 scale factor 10 e g scaled albedo 500 corresponds to albedo 50 The next section presents a simplified treatment of the radiation and heat fluxes in the energy balance 7 2 Surface energy balance Surface energy balance is an essential part of climatology The energy balance equation applicable to most land surfaces can be written as Asrar 1989 Rh G H LE 7 7 where Rn is the net radiant energy absorbed by the surface The net energy is dissipated by conduction into the ground G convection to the atmosphere H and available as latent heat of evaporation LE The amount of energy employed in photosynthesis in case of vegetated surfaces is usually small compared to the other terms Therefore it is neglected here The terms on the right hand
315. r of about 0 5 0 8 K per 0 01 emissivity error if the surface temperature is much higher than the boundary layer air temperature 94 An accuracy of 1 2 K can be achieved if the emissivity estimate is better than 2 15 Bibliography 1 Ackerman S A Strabala K I Menzel W P Frey R A Moeller C C and Gumley L E Discriminating clear sky from clouds with MODIS J Geophys Res Vol 103 D24 32 141 32 157 1998 2 Adler Golden S M Matthew M W Anderson G P Felde G W and Gardner J A 2002 An algorithm for de shadowing spectral imagery Proc 11th JPL Airborne Earth Science Workshop 5 8 March 2002 JPL Publication 03 04 Pasadena U S A 3 Asner G Canopy shadow in IKONOS satellite observations of tropical forests and savannas Remote Sensing of Environment 87 4 521533 doi 10 1016 j rse 2003 08 006 2003 4 Asrar G Fuchs M Kanemasu E T and Hatfield J L Estimating absorbed photosyn 110 111 112 A A A Lo thetically active radiation and leaf area index from spectral reflectance in wheat Agron J Vol 76 300 306 1984 Asrar G Theory and Applications of Optical Remote Sensing J Wiley New York 1989 Baret F and Guyot G 1991 Potentials and limits of vegetation indices for LAI and APAR assessment Remote Sensing of Environment Vol 35 161 173 1991 Berk A Bernstein L S Anderson G
316. radiance directory example e0_solar_kurucz2005_04nm dat Output is a new sensor subdirectory example sensor xxx_kurucz2005 where the first 10 char acters of the e0_solar_kurucz2005_04nm starting after the e0_solar_ are appended to the input sensor name The contents of the input atcor sensor xxx are copied to the output directory e0_solar_xxx spc is deleted in the output directory and replaced by the new e0_solar_xxx_kurucz2005 spc A comparison of e0_solar_xxx spc with e0_solar_xxx_kurucz2005 spc shows the influence of the change of the irradiance spectrum In addition a new atm_lib xxx_kurucz2005 is created where CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 154 Standard high resolution atmospheric database 540 2547 nm CHRIS Proba database smaller spectral coverage 380 108 nm High resolution database 1 Jexport data datar atcor 3 atm_database Solar irradiance file fl Yexportedata data atcor 3 atm_database eQ_solar_kurucz2005_Odnm dat Solar irradiance file f2 Vexport data data atcor2 3 sun_irradiance eQ_solar_thu2003_PSL_ku2005_Odnm dat High resolution database 2 Yexport data datay atcor 3 atm_database_thuZ003_PS Convert Database 1 irradiance f1 into Database 2 irradiance f2 Input database corresponding to eO_solar_kurucz2005_Od4nm export datardata atcoret atm_databazer Output database corresponding to eb_solar_thu2b0a_RSL_ku200
317. rage additional correction factor is applied to the border pixels in order to correct for spectral variation of the brightness difference Interpolate Borders The border pixels are interpolated from their neighbours overwriting the original values Shadow Border Width the with of the border to be corrected interpolated Output A cube containing the filtered image data is generated and the ENVI header is copied to the new file CHAPTER 5 DESCRIPTION OF MODULES 106 Figure 5 64 Shadow border removal tool CHAPTER 5 DESCRIPTION OF MODULES 107 5 7 Menu Simulation The Simulation menu provides programs for the simulation of at sensor radiance scenes based on surface reflectance or emissivity and temperature images Satellite ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Licensed for Daniel TOA At Sensor Radiance Cube input reflectance At Sensor Apparent Reflectance Resample Image Cubet n channels gt m lt n channels Figure 5 65 Simulation modules menu 5 7 1 TOA At Sensor Radiance Cube This routine calculates an At Sensor Radiance Cube from an reflectance image cube All parameters used for the processing are generated from the x inn file of the input cube If the function is called the cube is opened and the x inn file is read which results in an at sensor cube _toarad bsq Note that this routine does not consider adjacency effects and is a simple forward propagation base
318. ral bands and updates the dis play File Display TIFF Image Loads a multi band TIFF image in a new window File Close Closes the window Edit Equalize Image Performs a histogram equalization on the three bands Edit Scale Image Applies standard linear scaling on the imagery on 5 levels Edit Scale Zoom Applies standard linear scaling on the imagery on 5 levels based on the statistics of the Zoom Window Edit No Scaling Reverts to unscaled display of the image Edit Scale to Range Scales a single band image linearly to a range entered as lower and upper limit only applicable in single band displays Edit Load Color Table Applies standard linear scaling on the imagery on 5 levels Profile Horizontal Opens a window for a horizontal profile through the image of the first band only The profile is updated for the cursor location in the zoom window whenever the zoom window is clicked Profile Vertical Opens a window for a vertical profile through the image of the first band only Profile Spectrum Opens a window for a spectrum of the image for images with 4 and more bands only Export Allows to export the currently displayed image to one of the given image data formats The displayed image may be exported as a scaled 8bit 24bit image to the available standard image formats Note when clicking in the zoom window the current image value and location is displayed or a small plot of the spectrum at this pixel location is created
319. rameter So the water vapor and to a smaller de gree the visibility determine the values of Lyatn and T In case of coregistered bands in the solar and thermal spectrum the water vapor and visibility calculation may be performed with the solar channels In addition if the surface emissivity is known the temperature T can be computed from eq 3 2 using Planck s law For simplicity a constant emissivity e 1 0 or e 0 98 is often used and the corresponding temperature is called brightness temperature The kinetic surface temperature differs from the brightness temperature if the surface emissivity does not match the assumed emissivity With the assumption e 1 0 the kinetic temperature is always higher than the brightness temperature As a rule of thumb an emissivity error of 0 01 one per cent yields a surface temperature error of 0 5K For rugged terrain imagery no slope aspect correction is performed for thermal bands only the elevation dependence of the atmospheric parameters is taken into account Chapter 4 Workflow This chapter familiarizes the user with ATCOR 2 3 s workflow and with the program s basic functionality using the graphical user interface A detailed description of all modules and user interface panels is given in the subsequent chapter 5 ATCOR may also be used in batch mode for most of its functions A description of the batch mode can be found in chapter 6 4 1 Menus Overview To start ATCOR double cli
320. re vegetated or partially vegetated fields e 0 96 0 99 agricultural soil e 0 95 0 97 water e 0 98 and asphalt concrete e 0 95 0 96 Emissivities of various surfaces are documented in the literature Buettner and Kern 1965 Wolfe and Zissis 1985 Sutherland 1986 Salisbury and D Aria 1992 The atmospheric longwave radiation Ratm emitted from the atmosphere toward the ground can be written as Ratm Ea C T 7 12 where is the air emissivity and T is the air temperature at screen height 2 m above ground sometimes 50 m above ground are recommended For cloud free conditions Brutsaert s 1975 equation can be used to predict the effective air emissivity 1 24 Co 7 13 CHAPTER 7 VALUE ADDED PRODUCTS 141 Here Puw is the water vapor partial pressure millibars hPa and Ta is the air temperature K Figure 7 1 shows Pwy as a function of air temperature for relative humidities of 20 100 The partial pressure is computed as Pwo RH es 100 7 14 where RH is the relative humidity in per cent and e is the water vapor partial pressure in saturated air Murray 1967 a Ty 273 16 Es Ta s0 T a ea edea To b The constants are a 17 26939 b 35 86 and eso es 273 16K 6 1078 hPa An alternative to equation 7 13 is the following approximation Idso and Jackson 1969 which does not explicitly include the water vapor and holds for average humidity condi
321. reen PWHM 2 8 nm read PWHM 10 0 nm 100 K1997 F2011 F2011 10 4 ua J G Eor 4 5 0 9 Wavelength perm black FWHM 0 4 nm green FWHM 2 8 nm red PWHM 10 90 om 100e K1997 F20113 F2011 1 0 es 1 4 1 6 1 6 2 aE 4 Wavelength perm 254 1 0
322. reflectance CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 17 The apparent reflectance describes the combined earth atmosphere behavior with respect to the reflected solar radiation mL 2 E cos0 eo p apparent where d is the earth sun distance in astronomical units L cg cl DN is the at sensor radiance Co C1 DN are the radiometric calibration offset gain and digital number respectively E and 0 are the extraterrestrial solar irradiance and solar zenith angle respectively For imagery of satellite sensors the apparent reflectance is also named top of atmosphere TOA reflectance 2 1 Radiation components We start with a discussion of the radiation components in the solar region i e the wavelength spectrum from 0 35 2 5 um Figure 2 2 shows a schematic sketch of the total radiation signal at the sensor It consists of three components 1 path radiance Lj i e photons scattered into the sensor s instantaneous field of view with out having ground contact 2 reflected radiation L2 from a certain pixel the direct and diffuse solar radiation incident on the pixel is reflected from the surface A certain fraction is transmitted to the sensor The sum of direct and diffuse flux on the ground is called global flux 3 reflected radiation from the neighborhood L3 scattered by the air volume into the current instantaneous direction the adjacency radiance As detailed in 74 the adjacency radiation L
323. reflectance T clear T gt haze line 20 reduce over under correction in cast shadow O no 1 yes Note on the non linear influence of vegetation in water vapor calculations This option applies to the APDA regression algorithm and only if the 940 nm region is selected for the water vapor retrieval The retrieval is based on a linear interpolation across the absorption region and errors can occur due to the non linear behavior of the reflectance of vegetated surfaces in this region A simple empirical correction to the water vapor map W is applied using the NDVI calculated with the apparent reflectances in the red NIR channels W new W old 0 1 VDVI 0 7 cm 9 2 CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 160 The correction is only performed for pixels with NDVI gt 0 25 and values NDVI gt 0 7 are reset to 0 7 Note on cut off limit for surface reflectance The default limit is set to a high value 150 reflectance to avoid a truncation in spectra of high reflectance surfaces e g snow and or surfaces with specular reflectance The previous default cut off before 2012 was set to 90 Note on factor b Factor b is a relative saturation factor applied to the maximum radiometric encoding e g for 8 bit data and b 1 all pixels with DN 255 will be marked as saturated in the _out_hcw bsq file color coding red Setting b 0 9 implies pixels with DN gt 230 will be considered as saturated or in th
324. res B Comparison of Solar Irradiance Spectra 179 180 180 181 181 181 182 182 183 185 185 192 194 195 196 201 205 208 208 208 214 216 216 217 217 218 220 223 229 229 231 234 239 238 238 241 241 243 250 253 List of Figures 24 ae 2 3 24 2 0 2 6 PA PAs 20 2 10 3 1 3 2 4 4 2 4 3 4 4 4 5 4 6 A 7 4 8 4 9 4 10 4 11 4 12 4 13 4 14 4 15 4 16 4 17 5 1 5 2 5 3 Visibility AOT and total optical thickness atmospheric transmittance Schematic sketch of solar radiation components in flat terrain Wavelength shifts for an AVIRIS scene 2 0 2 a Radiometric calibration with multiple targets using linear regression Sketch of a cloud shadow geometriy aoa aoa a e e e a a De shadowing of an Ikonos image of Munich 0200 Zoomed view of central part of Figure 2 6 0 Nadir normalization of an image with hot spot geometry Left reflectance image without BRDF correction Right after empirical BRDF correction BRDF correction in rugged terrain imagery Left image without BRDF correction Center after BRDF correction with threshold angle 6r 65 Right illumination i el hee Eee E ee a ee Effect of BRDF correction on mosaic RapidEye image DLR Atmospheric transmittance in the thermal region 0 004 Radiation components in the thermal regio
325. ribution function BRDF Nicodemus 1970 Slater 1985 The ground is illuminated hemispherically by the direct and diffuse solar flux and the reflected radia tion is recorded from a certain direction i e hemispherical input radiation directional reflected radiation Since the reflected radiation is always measured in a small cone the term hemispherical conical reflectance factor HCRF is also used but for small instantaneous field of view sensors directional is a sufficiently accurate geometrical description However for simplicity we will use the abbreviation reflectance in this manual In spectral regions dominated by scattering effects the terms of equation 10 1 are calculated with the scaled DISORT option discrete ordinate radiative transfer 60 in regions with strong CHAPTER 10 THEORETICAL BACKGROUND 187 atmospheric absorption the more accurate correlated k algorithm is used in combination with DISORT 7 The results are stored in look up tables LUT Since MODTRAN calculates the path radiance including the diffuse reflected ground radiation in the form Tait Egl0 p m 1 ps Lath Pp T Lpatn 0 Lath 0 Tdi f E p p T 10 2 two MODTRAN runs with surface reflectance p 0 and pp 0 15 are required to calculate the diffuse ground to sensor transmittance Tg and spherical albedo s from equation 10 2 natn er Lpatn 0 7 K pr Eslpr 10 3 Es pr ante 10 4 _ E 0 m 1 7
326. rsampling factor of 3 pixels A high angular resolution is more important than a low undersampling factor 5 3 5 Cast Shadow Mask The calculation of the cast shadow map is done by ATCOR after reading the DEM files If the shadow map is computed on the fly it is kept in memory and it is not stored as a separate file If the user wants to inspect the DEM shadow map the program shadow has to be started before running ATCOR The program accepts float values of the solar zenith and azimuth angles The output file name of the DEM cast shadow map includes the zenith and azimuth angles rounded to integer values The DEM cast shadow map is a binary file where shadow pixels are coded with 0 and sunlit pixels with 1 It includes self shadowing and cast shadow effects Self shadowing consists of pixels oriented away from the sun with slopes steeper than the solar elevation angle The cast shadow calculation is based on a ray tracing algorithm and includes shadow regions caused by higher surrounding mountains Figure 5 26 shows the GUI panel 5 3 6 Image Based Shadows This routine detects the fractional shadows in the image using a multi index based approach A floating point illumination file is saved to be used as input for atmospheric correction Figure 5 27 shows the GUI panel NOTE this function is found in the menu Filter for ATCOR versions without support for terrain correction CHAPTER 5 DESCRIPTION OF MODULES 12 m Ls e
327. rwise 0 and filter_type a number CHAPTER 4 WORKFLOW 42 cross track FOV degree pixels per line dummy to agree with airborne ATCOR first last reflective band 0 35 2 55 um first last mid IR band 2 6 7 0 wm first last thermal band 7 0 14 um flag for tilt capability 1 yes 0 no no gain settings required dummy temperature band itemp_band 77 Table 4 2 Sensor definition file instrument with thermal bands between 1 and 9 for the type of channel filter function compare section 4 6 1 and Fig 4 14 The filter types 1 to 8 are analytical functions filter type 9 is reserved for arbitrary user defined channel filter functions the band rsp files Center wavelength and bandwidth for each channel are defined in the wavelength file wl pertaining to the center pixel column of the detector array e For each spectral channel j the channel center wavelength Ae j depends on the image column or pixel position x The absolute value of A is specified in the wavelength file used to generate the spectral channel response functions and it is also included in the sensor specific solar irradiance file e g e0_solar_chris mode3 spc If n is the number of image columns the change A x j of the center wavelength A 7 with the pixel position x can be described as a 4th order polynomial using the nm unit A z J nm ao j a1 j a2 j 2 a i x a i 2 4 1 Ac x j
328. s Vol 46 2803 2816 2007 21 ERSDAC ASTER User s Guide Part II Vers 3 1 2001 22 Fontenla J M Curdt W and Haberreiter M Harder J and Tian H Semiempirical Models of the Solar Atmosphere III Set of Non LTE Models for Far Ultraviolet Extreme Ultraviolet Irradiance Computation The Astrophysical Journal 707 482 502 2009 123 Ll Fontenla J M Harder J Livingston W Snow M and Woods T High resolution solar spectral irradiance from extreme ultraviolett to far infrared J Geophys Res Vol 116 D20108 31pp 2011 24 Lol Fraser R S Bahethi O P and Al Abbas A H The effect of the atmosphere on classifi cation of satellite observations to identify surface features Remote Sens Environm Vol 6 229 249 1977 25 Gao B C Kaufman Y J Han W and Wiscombe W J Correction of thin cirrus path radiances in the 0 4 1 9 um spectral region using the sensitive 1 375 um cirrus detecting channel J Geophys Res Vol 103 D24 32 169 32 176 1998 126 LL Gao B C Yang P Han W Li R R and Wiscombe W J An algorithm using visible and 1 38 um channels to retrieve cirrus cloud reflectances from aircraft and satellite data IEEE Trans Geosct Remote Sens Vol 40 1659 1668 2002 27 Gao B C Kaufman Y J Tanre D and Li R R Distinguishing tropospheric aerosols from thin cirrus clouds for impr
329. s brightness gradients in the image These effects can be removed with an empirical method that normalizes the data to nadir reflectance values In addition for rugged terrain areas illumi nated under low local solar elevation angles these effects also play a role and can be taken care of with an empirical method included in the ATCOR package The ATCOR software was developed to cover about 80 of the typical cases with a reasonable amount of coding It is difficult if not impossible to achieve satisfactory results for all possible cases Special features of ATCOR are the consideration of topographic effects and the capability to process thermal band imagery There are two ATCOR models available one for satellite imagery the other one for airborne imagery 77 78 The satellite version of ATCOR supports all major commercially available small to medium FOV sensors with a sensor specific atmospheric database of look up tables LUTs con taining the results of pre calculated radiative transfer calculations New sensors will be added on demand The current list of supported sensors is available at this web address A simple interface has been added to provide the possibility to include user defined instruments It is mainly intended for hyperspectral sensors where the center wavelength of channels is not stable and a re calculation 12 CHAPTER 1 INTRODUCTION 13 of atmospheric LU T s is required e g Hyperion Chris Proba An in
330. s is put on simple models based on the reflectance temperature cube derived during the atmospheric correction No additional data with the exception of air temperature is taken into account All value added products are written to a file with up to 11 bands The file structure is band sequential If the input file name is example bsq the output reflectance file name is example_atm bsq and the value added file name is example_atm_flx bsq the flx indicating the most important part of the calculation 1 e the radiation and heat fluxes 7 1 LAI FPAR Albedo Many vegetation indices have been introduced in the literature Only two are presented here because these are often used for simple parametrizations of the leaf area index LAI the fraction of absorbed photosynthetically active radiation FPAR and surface energy fluxes Baret and Guyot 1991 Choudury 1994 The normalized difference vegetation index NDVI is defined as NDVI 850 650 Gi P850 P650 where peso and pg50 are surface reflectance values in the red 650 nm and NIR 850 nm region respectively The soil adjusted vegetation index SAVT is defined as Huete 1988 Baret and Guyot 1991 with L 0 5 Ps50 Peso 1 5 SAVI pg50 peso 0 5 7 2 The leaf area index LAI can often be approximated with an empirical three parameter relationship employing a vegetation index VI SAVI or VI NDVI VI ay a exp az LAT
331. s on each side E Smoothing Factor 0 no smoothing El Polishing Filter Type wv Neighbour Derivatives w Lowpass Filter w Savitzky Golay Define Polished Output Data Cube data hyper ion Bern_02 Hyper ion_sub67_poliish bsq Help Run Polishing Done B Figure 5 59 Statistical spectral polishing 5 6 4 Spectral Polishing Radiometric Variation A module that was originally developed for the airborne version of ATCOR is the spectral polishing The algorithm is only intended for hyperspectral imagery INFUT FILE natn Vdata atcord demo_data dais99 bard_tope dais_bard_atm bsq OUTPUT IMAGE FILE 3 data atcord2 demo_datardais9 barl_topo dais_bard_ata_polish bsg J OVERWRITE Output file already exists change name or press OWERWRITE Quit Figure 5 60 Radiometric spectral polishing Input to the spectral polishing program is the reflectance cube calculated with ATCOR It em ploys the vegetation index 0 lt NDVI lt 0 33 NDVI PNIR PRED PNIR PRED to mask soil pixels A soil spectrum is a slowly varying function of wavelength therefore a spectral smoothing will only remove spikes without disturbing the spectral shape Then the average re flectance spectrum over all soil pixels is calculated and smoothed with a 5 channel filter except for the atmospheric water vapor regions where a linear interpolation is performed The ratio of the filtered to the original soil spectrum is the spe
332. s that 1t is hardly feasible to define generic class limits for any kind of data acquisition and sensor The calibration follows the scheme shown in Figure 10 28 Differences in limits can be attributed to the fact that the higher resolution images allows for a more accurate and statistically more relevant calibration of the model whereas for lower resolution the number of classes should be reduced The classes can be denominated as water artificial materials soils sparse vegetation grassland and forests For each of the classes the optimum kernel weights are calculated and stored for each image of a campaign All weights are then averaged while bad fitting classes with relative errors greater than 10 are excluded from averaging No BRDF correction is applied for classes without any fitting parameters i e if less than 3 bands out of 4 within the class could be calibrated The averaged model is stored for later application to the imagery Image correction Finally the derived BRDF model calibration data are to be applied to the image data For applica tion on the imagery the BCI has to be calculated from each image and is used to get a continuous correction function HDRF image ATCOR output BRDF cover index BCI scan angle file zenith azimuth image meta data calibrated model derive model interpolate model kernel subset to BCI range calculate model weighting factors for each pixel combine into relative a
333. s the corresponding GUI panel The BREFCOR was originally implemented for the airborne version of ATCOR for sensors with a large FOV It has been adapted specifically for the processing of image mosaics if the scenes have been acquired at various tilt and solar zenith angles It may e g be used for processing RapidEye or Worldview imagery The BREFCOR software is delivered as part of ATCOR for correction of observer BRDF effects using an unique cover dependent approach For the satellite version the correction works on a number of atmospherically corrected reflectance images e g before doing some mosaicking Two major options for correction are available e Model based The Ross Thick Li Sparse reciprocal BRDF model kernels are used for cor rection of the imagery The various viewing angles of the individual images provided are used as reference points to calibrate the kernel weighting factors and the isotropic component of the BRDF models This model requires a sufficient number of images for good calibration as a variety of incidence and viewing angles are required e Empirical If the number of images is small the model based correction does not lead to good results The empirical BRDF correction simply adjusts the mean of surface cover types to a global mean The following inputs are necessary Inputs Files A list of atmospherically corrected input files has to be compiled _atm bsq The ATCOR inn file
334. s the fraction of the direct illumination for each pixel in the p vector i e the complete scene without cloud and water pixels However since the matched filter is not a perfect shadow transformation it is much better to restrict its application to the potential most likely CHAPTER 10 THEORETICAL BACKGROUND 226 shadow areas This is an important processing step to reduce the number of mis classifications or false alarms If omitted it will cause strange shadow pixels scattered all over the image An example can be found in the central part of Fig 10 24 where the standard shadow map contains a lot of artifact shadow areas Figure 10 24 Cloud shadow maps of a HyMap scene Left surface reflectance image of HyMap at Chinchon Spain 12 July 2003 Colour coding RGB 878 646 462 nm channels Center standard shadow map showing a lot of artifact shadow areas grey patches which do not appear with the core shadow approach right part Right improved cloud shadow map derived from core shadow regions Therefore the proposed method tries to find the core shadow areas in a scene and subsequently expands the core regions to obtain the final mask that includes a smooth shadow clear transition The physically scaled shadow function is then applied only to the pixels in the final mask The histogram of the unscaled shadow function can be employed to separate regions of low values of Y from the moderate to high values compare Fi
335. s the parameter npref 1 variable visibility see line 20 below If npref 0 it is reset to npref 1 In the interactive mode the user can just press the Aerosol Type button on ATCOR s main panel to execute the aerosol type retrieval irrespective of the atm name in the inn file line 14 temfile atmospheric LUT file name thermal region empty if no thermal band line 15 1 0 adjacency range km line 16 35 0 visibility km line 17 0 7 mean ground elevation km asl not used in case of rugged terrain where elevation file applies line 18 33 0 178 0 solar zenith solar azimuth angle degr line 19 10 0 150 0 off nadir sensor tilt angle sensor view azimuth angle degr For nadir looking sensors the tilt angle is zero and the view azimuth is an arbitrary value line 20 0 0 1 0 0 0 1 npref iwaterwv ihaze iwat_shd ksolflux ishadow icl_shadow seven parameters controlling the processing options npref 0 constant visibility npref 1 variable visibility based on dark reference areas in the scene npref 1 variable visibility for each sub image during batch job with tiling iwaterwv 0 no water vapor correction or no water vapor bands available CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 164 iwaterwv 1 water vapor correction using bands in the 940 nm region iwaterwv 2 water vapor correction using bands in the 1130 nm region iwaterwv 3
336. s the project order and the following number the project number T he meta data include the geographic coordinates and the solar elevation and azimuth angles The sensor can tilt into any direction and the satellite geometry as viewed from the scene center is specified with e Nominal Collection Azimuth absolute azimuth view angle e g east 90 e Nominal Collection Elevation ATCOR s tilt angle can be calculated from equation 9 9 with the Ikonos orbit altitude 680 km The tilt angle is close to the incidence 90 elevation see Table 9 1 elevation degree incidence degree tilt degree Table 9 1 Elevation and tilt angles for Ikonos Ikonos offers a radiometric calibration L DN calcoef where calcoef is specified in the in band ra diance unit mWcem sr see http www spaceimaging com products ikonos spectral htm For post 22 February 2001 scenes with 11 bit data calcoef is specified as 728 727 949 and 843 blue green red NIR band respectively These values have to be converted into cl 1 calcoef bandwidth and are stored in the ikonos_2001_std cal standard calibration file 9 6 8 Quickbird The metadata files are named IMD They contain the geographic coordinates The sunEl and sunAz keywords or meanSunEl and meanSunAz specify the solar elevation and azimuth angle respectively Similar to Ikonos the sensor can tilt into any direction The satellite
337. s the spectral sampling interval Type of Filter Function The type defines the basic shape of each of the created response curves Options are Butterworth Order 1 slow drop off Butterworth Order 2 close to Gauss Butterworth Order 3 between Gauss Rect Butterworth Order 4 close to Rectangular Gaussian Rectangular Triangular Decreasing Binning from Rectangular to Triangular Spectral Binning Factor This factor allows binning of channels e g a factor 4 will combine four spectral response functions to produce the equivalent new channel filter function This is a convenient feature for programmable instruments e g HySpex or Specim AISA For using this function a wavelength reference file containing the unbinned spectral response is required as an input Outputs A numbered series of band_ rsp files are written to the path indicated by the output directory The files contain wavelength reference and the relative response in two ASCII formatted columns 5 2 3 Apply Spectral Shift to Sensor This is a tool to shift the spectral response of a predefined sensor or to change the FWHM values of the sensor based spectral position and FWHM detection results from the ATCOR smile detection routine Inputs e input sensor definition file sensor_ dat e smile file from smile detection routine or FWHM file smile_poly_ord4 dat or smile_poly_ord4_fwhm dat e New Sensor Name directory name of new sensor Outputs A new
338. same as the function Profile Spectrum of above The menu in the such loaded window allows to save the spectrum to an ASCII table to adapt the graph s properties and font size configure the display and to output the graph to an appropriate graphics format CHAPTER 5 DESCRIPTION OF MODULES LE f na sd n OZ 3 f 1b sc oof uc La ey perion DET ia m PENO Je _ 1b SS wor z a ba v v a 3 Figure 5 4 Display of ENVI imagery E o a Spectral Plot of Pixel 128 206 1090 1500 2006 Wavelength 92 CHAPTER 5 DESCRIPTION OF MODULES 59 5 1 2 Show Textfile Use this function if you need to edit a plain text file which comes together with the data to be processed The file is opened in a simple editor and may be changed and saved The function comes handy if an ENVI header needs to be checked or updated e g Selecting the Save or the Save As function in the submenu will allow to overwrite the file or to create a new one Aa y X src_idl atcor atcor_23 demo_data tm_rugged tm_blforest hdr File Help i Save A save AS Landsat 5 TM Black Forest 12 Sept 1995 solar zen 49 0 Print Setup i zolar azim 14b deg Print 0 Tone t ile type EMV Standard data type 1 interleave bq sensor type Unknown byte order 1 band names 4 band 1 band 2 band 3 band 4 band 5 wavelength 4 0 486 0 570 0 661 0 838 1 55 ril s eg Figure 5 5 Simp
339. sensor definition is created which is shifted against the original sensor by the values given in the smile definition file The shift is calculated as across track average of the band wise polynomial All components of old sensor other than the response files are copied to the new sensor directory One may use this function for a constant offset by creation of a text file with the number of lines number of bands and the second column containing the spectral offset to be applied CHAPTER 5 DESCRIPTION OF MODULES 64 Generate Spectral Filter Functions V3 0 2015 Envi Header hdr or 3 Columns band number center wavelength bandwidth micron or nm Wavelength File src_idl atcor atcor_4 deno_data Huspex_ATCOR_deno FL3_VNIR_ge0 hdr Select Type of Filter Function 27 1 Butterworth order 1 slow drop off 22 Butterworth order 2 close to Gauss se 3 2 Butterworth order 3 between Gaues rectangular se 4 Butterworth order 4 close to rectangular se t Gauss ae B 1 Rectangular wef t Triangular se t Shape changes from near rectangular first bands to triangular last bands due to binning Spectral Binning Factor p Output Directory src_idl atcor atcor_4 sensor Hyspex_VNIR_1800 os HELP Generate Filter Files rsp QUIT Figure 5 15 Spectral Filter Creation D x Apply Spectral Shift to Sensor Select Input Sensor Definition Yerc_id atcor atcor_4 sensor APEX_2015_L1 sensor_
340. shadowed DN image for channel k L k co k DN 5 10 121 Figure 10 25 shows an example of de shadowing More images with the results of the de shadowing method can be found on ATCOR s web page http www rese ch or http www op dlr de atcor CHAPTER 10 THEORETICAL BACKGROUND 229 Figure 10 25 De shadowing of a Landsat 7 ETM scene Subset of a Landsat 7 ETM scene from Kenia 10 April 2001 Color coding RGB bands 4 2 1 830 560 480 nm Left original scene right after de shadowing 10 6 Correction of BRDF effects The bottom of atmosphere reflectance as retrieved after standard ATCOR atmospheric compen sation is highly variable due to the influence of the bidirectional reflectance distribution function BRDF 61 The observed reflectance value may deviate from the average spectral albedo by up to 30 specifically for vegetation and man made surfaces ATCOR offers three different methods of correcting BRDF effects The first method is mainly intended for flat terrain and normalizes the off nadir reflectance values to the corresponding nadir values The second method is exclusively dedicated to rugged terrain imagery and corrects for BRDF effects due to the variability of the solar incidence angle The reflectance values of areas with low local solar elevation angles i e large local solar zenith angles are often overcorrected by the assumption of isotropically reflecting surfaces The method reduces
341. signment to the vegetation soil class is performed on the fly in memory employing the vegetation index red and NIR bands required and the 3 class emissivity map is available file mage_atm_emi3 bsq compare chapter 4 5 In the airborne ATCOR version 78 a pre classification with more emissivity classes can be used as already suggested in 64 e for multispectral thermal bands the normalized emissivity method NEM or adjusted NEM are also implemented In the NEM 29 the surface temperature is calculated for all chan nels with a constant user defined emissivity and for each pixel the channel with the highest temperature is finally selected In the adjusted NEM ANEM 15 the assigned emissivity is surface cover dependent Here we define four surface cover classes water vegetation soil dry vegetation sand asphalt based on the following criteria vegetation Pnir Pred gt 2 and Pnir gt 0 20 soil dry vegetation Pnir Prea gt 1 4 and Pnir Preg lt 2 0 and prea gt 0 09 sand asphalt Pnir Prea lt 1 4 and prea gt 0 09 water Pnir lt 0 05 and P1 6um lt 0 03 To each class the user can assign an emissivity valid for the channel with the highest temper ature There is only one common emissivity class in case of night data or data from purely CHAPTER 10 THEORETICAL BACKGROUND 199 thermal channels The ANEM method provides accurate channel emissivities and surface temperatures if the cl
342. sor radiance DN standard dev DN Radiance is in mWem sr um The standard deviation indicates the spatial uniformity within the box e angles sun month day hour minute lat lon Calculate solar position from parameters the keyword are self explanatory The output angles is an array containing zen azi doy Zenith angle and azimuth angle in degrees and day of the year as a number 6 4 Meta File Reader Reading of the sensor meta data is supported for most of the pre defined sensors The prerequisites are that the radiometric calibration offset gain values are included in the file and that one meta file exists for each scene a multiple image meta file is not supported The meta file reader extracts the required information for an ATCOR run and creates two files metafile cal contains the radiometric calibration for each band metafile inn contains all input parameters for the ATCOR run Note as the meta file reader does not know the average scene elevation the default elevation is set to 0 sea level and the visibility will be calculated from the scene i e parameter npref 1 in the inn file However these defaults can be modified using the keywords parameters given below The default atmosphere is US Standard In rugged terrain the topography files elevation slope aspect are also needed on lines 8 10 of the inn file In this case the file has to be modified subsequ
343. spectrome try data Part 1 parametric orthorectification Int J Remote Sensing Vol 23 2609 2630 2002 89 Schl pfer D PARGE User Guide Version 3 1 ReSe Applications Schlapfer Wil Switzer land 2011 90 Schl pfer D Richter R and Feingersh T Operational BRDF Effects Correction for Wide Field of View Optical Scanners BREFCOR IEEE Trans Geoscience and Remote Sensing vol 53 no 4 pp 18551864 2014 References 249 91 Schowengerdt R A Remote Sensing Models and Methods for Image Processing 3rd Edition Elsevier Academic Press 2007 92 Shao Y Taff G N and Walsh S J Shadow detection and building height esti mation using IKONOS data International Journal of Remote Sensing 32 22 69296944 doi 10 1080 01431161 2010 517226 2011 93 Le Sirguey P Simple correction of multiple reflection effects in rugged terrain Int J Remote Sensing Vol 30 1075 1081 2009 94 haa Slater P N Remote Sensing Optics and Optical Systems Addison Wesley London 1980 o Slater P N Radiometric considerations in remote sensing Proc IEEE Vol 73 997 1011 1985 96 LL Slater P N et al Reflectance and radiance based methods for the in flight absolute cali bration of multispectral sensors Remote Sensing of Environment Vol 22 11 37 1987 97 Soenen S A Peddle D R and Coburn C A
344. steps are employed in the ground reflectance calculation Step 1 The influence of the neighborhood adjacency effect is neglected and the surface reflectance is obtained from 1 _ m d co e DN Lp TuEg pr 0 15 p 10 8 where the spectral band index is omitted for clarity The factor d takes into account the sun to earth distance d is in astronomical units since the LUTs with path radiance and global flux are calculated for d 1 in ATCOR Step 2 The second step calculates the average reflectance in a large neigborhood of each pixel range R 0 5 1 km Loa p N2 2 Pij 10 9 2H where N corresponds to the number of pixels for the selected range R of the adjacency effect 67 74 The exact choice of R is not critical since the adjacency influence is a second order effect Instead of the range independent weighting in eq 10 9 a range dependent function can be selected with an exponential decrease of the weighting coefficients 68 The range dependent case requires more execution time of course Except for special geometries the difference between both approaches is small because the average reflectance in a large neighborhood usually does not vary much and the influence is a second order effect p x y p x y ato plz y 10 10 The function q indicates the strength of the adjacency effect It is the ratio of the diffuse to direct ground to sensor transmittance The range dependent vers
345. t folder metafile ext SPOT 6 read_meta_theos input folder metafile ext 2 Formosat read_meta_theos input folder metafile ext 3 Theos read_meta_wv2 input folder metafile ext 1 QuickBird read_meta_wv2 input folder metafile ext 2 Worldview 2 8 4 bands or pan In several cases the format for different sensors is very similar so they are distinguished by a sensor identification number Example read_meta_wv2 input 1 invokes QuickBird while the number 2 invokes Worldview 2 For all programs the following optional keywords can be used ele 0 5 average scene elevation km default ele 0 vis 40 visibility km default vis 23 adj 0 adjacency range km default adj 1 Chapter 7 Value Added Products As a by product of atmospheric correction a number of useful quantities can readily be calculated The first group of value added products include vegetation indices based on surface reflectance instead of at sensor radiance simple parametrizations of the leaf area index and wavelength integrated reflectance albedo The second group comprises quantities relevant for surface energy balance investigations which are a useful supplement for studies in landscape ecology and related fields e g as input for regional modeling of evapotranspiration These include global radiation on the ground absorbed solar radiation net radiation and heat fluxes Emphasi
346. t It is intended for future hyper spectral space sensors such as EnMAP A separate CHRIS Proba database is included in the distribution containing calculations for the tilt angles 0 35 and 55 for seven equidistantly spaced relative azimuth angles 0 30 180 CHRIS data acquisition is usually close within 2 to this set of tilt angles and interpolation is automatically performed While the standard database is named atm_database the CHRIS database is named atm_database_chris and it is automatically accessed if the letters chris or CHRIS are part of the user defined sensor name e g chris moded5 While the standard database uses a 0 4 nm wavelength grid the CHRIS database employs a 1 nm grid 9 1 2 Thermal region The thermal high resolution database employs a spectral sampling distance of SSD 0 4 cm for the wavelength region 7 10 wm i e corresponding to a wavelength SSD 2 4 nm and SSD 0 3 cm for the wavelength region 10 14 9 um i e corresponding to a wavelength SSD 3 5 5 nm A triangular weight function is used with a spectral bandwidth of twice the SSD The Isaacs s 2 stream method is employed including the correlated k algorithm The Isaacs s algorithm is much faster than DISORT and yields the same results for our cases in the thermal region All files bt7 are calculated for view or scan angles from 0 nadir to 40 off nadir with a 5 increment t
347. t and prepare DEM data for the calculation 30 CHAPTER 4 WORKFLOW 31 Satellite ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Display ENYI File Show Text File lc DLR ReSe 2015 Version 9 0 0 Select Input Image Resize Input Image import O Export al RGBN Geo TIFF Plot Sensor Response NPGB Geo T1FF Plot Calibration File JPEG2O00 Geo Read Sensor Meta Data ENVI BIF Image Show System File ENYI BIL Image Edit Freferences ERDAS Imagine QUIT Landsat 8 OLI TIRS Landsat 8 OLI Hyperion Image TIF Hyperion Raw Image BSO Figure 4 2 Top level graphical interface of ATCOR File Satellite ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Licens Define Sensor Parameters Version 9 0 0 c DLR ReSe 2015 Generate Spectral Filter Functions Apply Spectral Shift to Sensor BBCALC Blackbody Function T fiL RESLUT Resample Atm LUTs from Monochr Database Figure 4 3 Top level graphical interface of ATCOR Sensor of slope aspect images from a digital elevation model for the skyview factor and for topographic shadow Furthermore it supports the smoothing of DEMs and its related layers see chapter 5 3 The menu ATCOR gives access to the ATCOR core processes for atmospheric correction in flat and rugged terrain supporting multispectral and hyperspectral instruments It also allows the haze removal on raw DN data and th
348. t is currently not implemented in the ATCOR environment In addition the method requires data with a very accurate spectral and radiometric calibration otherwise its potential advantage will be lost 2 Five water vapor grid points at 0 4 1 0 2 0 2 9 and 4 0 cm are sufficient to cover the 0 5 5 0 cm range with an accuracy of about 5 10 75 10 5 Non standard conditions The non standard situations refer to scenes with a substantial amount of haze and shadow areas The non standard atmospheric conditions treat the haze removal and de shadowing employing spec tral and statistical algorithms Although bidirectional surface reflectance effects are independent of the atmospheric conditions the subject is included here because the isotropic reflector is used for the standard conditions We present some methods of BRDF correction in flat and rugged terrain 10 5 1 Haze removal In many cases of satellite imagery the scene contains haze and cloud areas The optical thickness of cloud areas is so high that the ground surfaces cannot be seen whereas in hazy regions some information from the ground is still recognizable In ATCOR the scene is partitioned into clear hazy and cloud regions Here we will treat the low altitude boundary layer 0 3 km haze as opposed to high altitude cirrus Thin boundary layer haze can be detected with broad band mul tispectral instruments while a detection of thin cirrus requires specific narrow bands around
349. taken into account in the ATCOR model It is an approximation of the terrain reflected radiation It is obtained by weighting the reflected radiation in a 0 5 km surrounding of a pixel with the terrain view factor The terrain view factor iS Vierrain Y 1 Very x y and the sky view factor Vsky x y is calculated from the DEM as explained below The sky view factor is normalized to 1 for a flat terrain The reflectance is calculated iteratively The first step neglects the adjacency effect and starts with a fixed terrain reflectance of P vn 0 1 71 T d co DN a y Lpl z Ov Ty Z O b z y EsTs 2 cosP x Des Ej Un Y 2 EP z Vilos y The terms are defined as px y 10 15 X y horizontal coordinates corresponding to the georeferenced pixel positions Z vertical coordinate containing the elevation information from the DEM DN x y digital number of georeferenced pixel Dx 2 0v Q path radiance dependent on elevation and viewing geometry ie Oy eround to sensor view angle transmittance direct plus diffuse components mala Sun to ground beam direct transmittance B x y angle between the solar ray and the surface normal illumination angle b x y binary factor b 1 if pixel receives direct solar beam otherwise b 0 extraterrestrial solar irradiance earth sun distance d 1 astronomical unit E x y 2 diffuse solar flux on an inclined plane see equation 10 18
350. tection and removal in re motely sensed multispectral imagery IEEE TGRS Vol 52 5895 5905 2014 57 Mouroulis P Green R O and Chrien T G Design of pushbroom imaging spectrometers for optimum recovery of spectroscopic and spatial information Applied Optics Vol 39 2210 2220 2000 58 Moran M S Clarke T R Inoue Y and Vidal A Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index Remote Sensing of Environment Vol 49 246 263 1994 References 247 59 Murray F W On the computation of saturation vapor pressure J Applied Meteorology Vol 6 203 204 1967 60 Stamnes K Tsay S C Wiscombe W J and Jayaweera K Numerically stable algorithm for discrete ordinate method radiative transfer in multiple scattering and emitting layered media Applied Optics Vol 27 2502 2509 1988 61 Nicodemus F E Reflectance nomenclature and directional reflectance and emissivity Ap plied Optics Vol 9 1474 1475 1970 62 Parlow E Net radiation of urban areas Proc 17th EARSeL Symposium on Future Trends in Remote Sensing Lyngby Denmark 17 19 June 1997 pp 221 226 Balkema Rotterdam 1998 163 Riano D Chuvieco E Salas J and Aguado I Assessment of different topographic corrections in Landsat T M data for mapping vegetation types IEEE Trans Geoscience and Remote
351. tegral part of all ATCOR versions is a large database containing the results of radiative transfer calculations based on the Modtran 5 code Berk et al 1998 2008 While ATCOR uses the AFRL MODTRAN code to calculate the database of atmospheric look up tables LUT the correctness of the LUT s is the responsibility of ATCOR Historical note For historic reasons the satellite codes are called ATCOR 2 flat terrain two geometric degrees of freedom DOF 65 and ATCOR 3 three DOF s mountainous terrain 68 They support all commercially available small to medium FOV satellite sensors with a sensor specific atmospheric database The scan angle dependence of the atmospheric correction functions within a scene is neglected here The airborne version is called ATCOR 4 to indicate the four geometric DOF s x y z and scan angle 71 It includes the scan angle dependence of the atmospheric correction functions a nec essary feature because most airborne sensors have a large FOV up to 60 90 While satellite sensors always operate outside the atmosphere airborne instruments can operate in altitudes of a few hundred meters up to 20 km So the atmospheric database has to cover a range of altitudes Since there is no standard set of airborne instruments and the spectral radiometric performance might change from year to year due to sensor hardware modifications a monochromatic atmo spheric database was compiled based on the Modtr
352. ter vapor absorption around 940 nm These effects are most likely caused by a spectral mis calibration In this case an appropiate shift of the center wavelengths of the channels will remove the spikes This is performed by an optimization procedure that minimizes the deviation between the surface reflectance spectrum and the corresponding smoothed spectrum The merit function to be minimized is n 0 u a T 2 11 i 1 where p 5 is the surface reflectance in channel i calculated for a spectral shift 6 ph is the smoothed low pass filtered reflectance and n is the number of bands in each spectrometer of a hyperspectral instrument So the spectral shift is calculated independently for each spectrometer In the currently implemented version the channel bandwidth is not changed and the laboratory values are assumed valid More details of the method are described in 32 A spectral re calibration should precede any re calibration of the radiometric calibration coefficients see section 5 8 6 for details about this routine Figure 2 3 shows a comparison of the results of the spectral re calibration for a soil and a vegetation target retrieved from an AVIRIS scene 16 Sept 2000 Los Angeles area The flight altitude was 20 km above sea level asl heading west ground elevation 0 1 km asl the solar zenith and azimuth CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 2l 20 O BU 15 te te 50 z 5 40 5 10 E E 3 30 2 E oo
353. terpolation 1940 2 linear 11400 1 no interpolation for 1400 1900 nm channels i1400 1 nonlinear interpolation 11400 2 linear e toarad input filename pixelsize pixelsize sz solar_zenith atmfile atmfile elev elevation vis visibility adjrange adjrange scalef scalef The keywords in brackets are optional the meaning of all keywords is described in chapter 8 Information on all missing keywords is taken from the corresponding inn file If the keyword elev is missing and the corresponding inn file contains the DEM files eleva tion slope aspect then the simulation is performed for a rugged terrain otherwise for a flat terrain compare chapter 8 e cal_regress ntargets 4 outfile regression4 This program uses the rdn files to calculate a regression for the c0 cl radiometric cali bration see chapters 2 3 and 5 4 9 The above example is for the case of n 4 targets and the output file will be regression cal in the directory of the rdn files which are prompted with a dialog pickfile panel A graphical user interface for this program is available in the Tools pull down menu of ATCOR labeled Calibration Coefficients with Regression e makeblue input filename The program computes a synthetic blue channel for sensors without a blue spectral band If it is started from the the interactive panel Tools a dialog pickfile box pops up prompting for the input file na
354. th radiance L1 i e photons emitted by the atmospheric layers emitted surface radiance L2 and reflected radiance L3 The short form of the radiance equation in the thermal region can be written as 40 L Ly 7 LDpp T 7 1 e F 7 10 34 where CHAPTER 10 THEORETICAL BACKGROUND 197 O E Figure 10 10 Radiation components in the thermal region L Lp L T Lgg T L3 7 1 e F r L at sensor radiance Li Lp thermal path radiance T ground to sensor atmospheric transmittance E surface emissivity T surface temperature LBB blackbody radiance at temperature T weighted with the channel s filter curve F thermal downwelling flux on the ground The second term on the right hand side of equation 10 34 is emitted surface radiance reaching the sensor the third term is the atmospheric radiance reflected at the surface and attenuated by the surface to sensor path The spectral band index elevation and angular dependence is omitted for brevity The Lgg T term is Planck s blackbody radiance B A T weighted with the spectral channel response function R A FBO TIROYA Lgn T 10 35 f R A dd Az For a discrete temperature interval T T T2 and increment e g Ti 200 K To 350 K increment 1 K equation 10 35 is solved numerically Temperature and radiance are then approx imated by an exponential fit function function with channel dependent coefficients a1 aa
355. the correlation coefficient the blue line represents the first of several iterations Papers on the cirrus algorithm often restrict eq 10 108 to the wavelength interval 0 4 lt A lt 1 um but we will extend this relationship into the SWIR region Substituting eq 10 108 into eq 10 107 yields Te A PA P A pel1 38pm y 10 109 Neglecting the cirrus transmittance Te i e setting Te 1 we obtain the cirrus path radiance corrected apparent reflectance image index cc EA A pel1 38um 7 10 110 As the cirrus is almost on top of the atmosphere we have pe 1 38um p 1 38um and the ap parent cirrus reflectance can be calculated with eq 10 105 Cirrus removal is conducted as the first step during atmospheric correction followed by the aerosol and water vapor retrievals If the average water vapor column W of a scene is less than some threshold default W 0 6 cm then the CHAPTER 10 THEORETICAL BACKGROUND 222 0 05 0 04 0 03 0 02 e CIR band 0 01 0 00 0 01 9 00 0 05 0 109 0 15 0 20 p RED band Figure 10 20 Scatterplot of apparent reflectance of cirrus 1 38 um band versus red band cirrus removal algorithm is switched off to avoid a misinterpretation of bright surfaces as cirrus in the 1 38 um channel Normally atmospheric water vapor completely absorbs surface features in the 1 38 um channel but the channel might become partly transparent to surface features for very l
356. the energy left for evapotranspiration The factor 286 in equation 7 19 converts the unit em day into Wm NDVI is the scaled NDVI ranging between 0 and 1 and truncated at 1 if necessary Equation 7 21 corresponds to equation 3 of Carlson et al 1995 with NDVIp 0 bare soil and NDVI 0 75 full vegetation cover The latent heat flux LE is computed as the residual LE R G H 7 22 A different heat flux model is employed for urban areas with man made surfaces asphalt concrete roofs etc These are defined here with the reflectance criteria P650 0 10 and P850 gt 0 10 and P650 gt P850 0 7 7 23 representing low vegetation indices with NDVI lt 0 176 This simple spectral definition is not unambiguous it might also apply to soils For urban areas the latent heat is usually very small and the fluxes G and H dominate Therefore the terms G LE and H are approximated by the following three equations Parlow 1998 G 0 4 Rn 7 24 LE 0 15 Rn G 7 25 H R G LE 7 26 For low vegetation indices SAVI lt 0 1 the ground heat flux G from equation 7 17 i e the vegetation model agrees well with G from equation 7 24 i e the urban model However major CHAPTER 7 VALUE ADDED PRODUCTS 143 differences exist for the LE and H terms see table 7 1 Parameters for this table are E 800 Rn 600 Fatm Rsurface 100 Wm T 30 C and Ta 20 C The veg and urb indic
357. the sensor is not supported as standard sensor by ATCOR typically for experimental purposes X Satellite ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Licens Define Sensor Parameters Version 9 0 0 c DLR ReSe 2015 Generate Spectral Filter Functions Apply Spectral Shift to Sensor BBCALC Blackbody Function T FL RESLUT Resample Atm LUTs from Monochr Database Figure 5 12 The Sensor Menu Fig 5 13 shows the three required steps to include a new user defined usually hyperspectral sensor to ATCOR The example uses a sensor with 96 spectral bands denoted as sensor_x96 A sub directory of atcor sensor has to be created named x96 and the three files as displayed in Fig 5 13 have to be placed in this sub directory This procedure is supported by the Function Define Sensor Parameters After execution of steps 1 and 2 the new sensor will be automatically detected when ATCOR is started Details about the sensor definition files are explained in chapter 4 5 Template files of several sensors are included in the distribution Gaussian Filter Files band01 rsp band02 rsp band96 rsp RESLUT Atm LUTs atm ATCOR Figure 5 13 Sensor definition files the three files on the left have to be provided created by the user CHAPTER 5 DESCRIPTION OF MODULES 61 5 2 1 Define Sensor Parameters This panel is the first step if a new sensor is to be defined
358. three iterations are usually sufficient to be independent of the start value of the terrain reflectance 68 However for highly reflective surfaces e g snow and high terrain view factors more than three iterations are necessary and a faster convergence of PA can be achieved with a geometric series for the terrain reflected radiation E as proposed in 93 EO E _ PE Viervain 10 16 L pte 1 Vterrain The next steps include the adjacency correction eq 10 9 10 10 and the spherical albedo effect eq 10 14 If O On s Py denote solar zenith angle terrain slope solar azimuth and topographic azimuth respectively the illumination angle can be obtained from the DEM slope and aspect angles and the solar geometry cosP x y cosOscosOn x y sinOssinOn x y cos ds nlx y 10 17 CHAPTER 10 THEORETICAL BACKGROUND 191 Geometry of solar illumination cos i cos 6 cos sin 6 sinh cost E Tir Py Direct irradiance a _ Circumsolar diffuse imidance isotropic diffuse gt flux Direct and diffuse radiation components Figure 10 6 Solar illumination geometry and radiation components The illumination image cos x y is calculated within ATCOR and stored as a separate map The diffuse solar flux on an inclined plane is calculated with Hay s model Hay and McKay 1985 also see Richter 1998 for the enhancement with the binary factor b Ez x y z Ea z brs z2 cosP x y cosOs
359. times be useful to identify areas in the scene where overcorrection exists This is usually caused by a too low visibility or in shadow areas line 3 5 0 pixel size m line 4 landsat4_5 Landsat 4 5 TM sub directory atcor2 3 cal landsat4_5 sensor text as defined in atcor2 3 bin sensor dat line 5 1 0 gain setting Any positive value is accepted this gain setting g is used to replace the c in the corresponding cal file with c g where g is the same for all channels line 6 calfile calibration file name line 7 0 9500 0 iemiss dem_unit surface emissivity DEM height unit iemiss surface emissivity option or value disregarded if no thermal band exists iemiss 0 invokes e 0 98 to be consistent with the definition of earlier ATCOR versions Since iemiss 1 is reserved for the cover dependent emissivity setting below e 1 0 has to be ap proximated as iemiss 0 999 or iemiss 0 9999 In case of multiple thermal bands this e holds for the thermal band itemp_band employed for the surface temperature evaluation see chapter 4 6 iemiss 1 fixed values of surface emissivity 0 98 water 0 97 vegetation 0 96 soil iemiss 2 same as iemiss 3 the iemiss 2 option of ATCORA is not supported here iemiss 3 NEM or ANEM method requires multiple thermal bands see chapter 10 1 5 iemiss 4 ISAC method requires multiple thermal bands see chapter 10 1 5 iemiss 5 both
360. tion 08804 184 LIST OF FIGURES 10 10 2 Visibility AOT retrieval using dark reference pixels 185 10 3 Radiation components illumination and viewing geometry 186 10 4 Schematic sketch of solar radiation components in flat terrain 187 10 5 Radiation components in rugged terrain sky view factor 190 10 6 Solar illumination geometry and radiation components 191 10 7 Combination of illumination map left with cast shadow fraction middle into con mous illunination feld right ss 46448 see een ee arre 192 10 8 Effect of combined topographic cast shadow correction left original RGB image right corrected image data source Leica ADS central Switzerland 2008 courtesy ORR ama a ae eo ae od ee oe we 193 10 9 Effect of cast shadow correction middle and shadow border removal right for BICIS ENCON s ss s ae sor ee eee SRR sad 194 10 10Radiation components in the thermal region 0200 08 2 eee 197 10 115chematic sketch of visibility determination with reference pixel 210 10 12Correlation of reflectance in different spectral regions 4 211 10 13Rescaling of the path radiance with the blue and red band 2 212 10 140ptical thickness as a function of visibility and visibility index 214 10 15Reference and measurement channels for the water vapor method 215 10 16APD
361. tion describes a de shadowing method based on the matched filter approach which is com plementary to the scene based method described in section 10 1 2 The proposed de shadowing technique works for multispectral and hyperspectral imagery over land acquired by satellite airborne sensors The method requires a channel in the visible and at least one spectral band in the near infrared 0 8 1 um region but performs much better if bands in the short wave infrared region around 1 6 and 2 2 um are available as well The algorithm consists of these major components i the calculation of the covariance matrix and zero reflectance matched filter vector ii the derivation of the unscaled and scaled shadow function iii a histogram thresh olding of the unscaled shadow function to define the core shadow areas iv a region growing to include the surroundings of the core shadow areas for a smooth shadow clear transition and v the de shadowing of the pixels in the final shadow mask Details are published in 72 p direct attenuated solar beam diffuse me Figure 10 21 Sketch of a cloud shadow geometry The method starts with a calculation of the surface reflectance image cube p p A where three spectral bands around A 0 85 1 6 and 2 2 um are selected These bands from the near and shortwave infrared region are very sensitive to cloud shadow effects because the direct part of the downwelling solar radiation flux at the gr
362. tion for a flat terrain but they lack the dependence on the spectral reflectance variations in the scene Therefore an image of the global flux is also provided that accounts for the spatial reflectance and visibility water vapor patterns VIS named scene_eglobal bsq Euar VIS z y Eaz p 0 VIS zx y E x y kese uP Y 10 29 CHAPTER 10 THEORETICAL BACKGROUND 196 Here p indicates a spatial averaging with a filter size corresponding to the specified adjacency range The unit of the global flux is mWem um and it is stored as float data 32 bits pixel Therefore its file size will be twice or four times the size of the input scene if the scene is encoded as 16bit pixel and 8bits pixel respectively For a rugged terrain images of the direct and diffuse fluxes will be calculated using the available DEM information on height z slope and aspect i e local solar illumination angle 8 and atmospheric conditions visibility water vapor VIS The direct flux on the ground is Edir z y d x y Eo Tsun VIS z Y z cosB x y 10 30 where Eo Tsun are extraterrestrial solar irradiance and sun to ground transmittance respectively and b is the topographic shadow mask 0O shadow 1 sunlit pixel The diffuse flux in mountainous terrain accounts for the adjacency effect and multiple reflection effects from the surrounding topography Using the terrain view factor V from the last section and the
363. tion of the solar zenith and azimuth angles spectral classification nadir normalization for wide field of view imagery adding of a synthetic blue channel for multispectral sensors with a blue band e g SPOT which is done for the atmospherically corrected surface reflectance image spectral calibration con version of the monochromatic atmospheric database from one to another solar irradiance spectrum and BIL to BSQ conversion Satellite AT C OR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Licensed for Daniel Version Solar Zenith and Azimuth SPECL Spectral Reflectance Classification SPECL for User Defined Sensors Add a Blue Spectral Channel Spectral Smile Detection Atm Absorption Features Spectral Calibration Atm Absorption Features Radiometric Calibrations included in ATCOR Calibration Coefficients with Regression Convert High Res Database New Solar Irradiance Convert atm for another Irradiance Spectrum MTF Modulation Transfer Function Figure 5 67 The tools menu 5 8 1 Solar Zenith and Azimuth The routine SOLAR_GEOMETRY is used to calculate the zenith and azimuth angle for the image location and acquisition time All explanations concerning the definition of angles are included in the panel Fig 5 68 Year T1939 Month i 6 Day 19 Hour UTC iat Minute fi 0 Second f 0 Geo Latitude f 48 12 Geo Longitude fi 11 30 degree Latitude positive is North of E
364. tions compare Figure 7 2 7 15 a 1 0 261 exp 7 77 x 107 273 T 7 16 Ln a al Ls ZO Water opor Partial Pressure hPa D 5 10 15 20 25 30 AD Air Temperature C Figure 7 1 Water vapor partial pressure as a function of air temperature and humidity Relative humidi ties are 20 to 100 with a 10 increment bottom to top curves respectively eq 7 14 The calculation of the heat fluxes G H and LE on the right hand side of equation 7 7 requires different models for vegetated and man made surfaces For vegetated or partially vegetated surfaces we employ a simple parametrization with the SAVI and scaled NDVI indices Choudury 1994 Carlson et al 1995 G 0 4 Ry SAV Im SAVI SAVIm 7 17 where SAV Im 0 814 represents full vegetation cover The sensible heat flux is computed as H B T Ta 7 18 B 286 0 0109 0 051 NDVI 7 19 CHAPTER 7 VALUE ADDED PRODUCTS 142 1 041 0 95 color Idso amp Jackson Brutadert 0 99 RH 3903 RH 70 En 0 85 RH 503 co ie E 0 80 0 25 0 20 0 65 O A 10 qa ZO 25 30 ABD Air Ternperature C Figure 7 2 Air emissivity after Brutsaert eq 7 13 and Idso Jackson eq 7 16 n 1 067 0 372 NDVI 7 20 i P850 P650 NDVI s ee 7 21 0 75 P850 P650 7 21 Equation 7 18 corresponds to equation la of Carlson et al 1995 because G is neglected there and so Rn G represents
365. to be directional reflectance to convert the image DNs to absolute reflectance values between 0 and 1 Output Definition The directory for all outputs the BRDF model file name and the file name appendix can be defined here Actions e Calibrate Model the model calibration file is created without applying a correction to the images e Run Model Based the model based BRDF correction is performed e Run Empirical the empirical BRDF correction is performed 5 5 2 Nadir normalization Wide FOV Imagery This module NADIR_REFL performs an empirical BRDF correction by normalizing the across track radiance gradients to the nadir brightness value see chapters 2 5 10 6 1 Figure 5 54 shows the corresponding GUI panel The nadir normalization was originally implemented for the airborne version of ATCOR and re quires a minimum field of view of 20 Therefore it might also be of interest for some of the supported satellite sensors e g IRS 1C WiFS or MERIS INPUT TRACE Reflectance Radiance data7 ateord2 deno_data hynep98 barl map bard atn beq OUTPUT HAGE nadir normalized Vdata atcer42 dema_data tynapSti ber hnap _bari_atn nadir beq HELF sf Input mage MOT Geocoded y Deocoded Input mage Sensor total fueld of view FOV deges 160 0 Global noraalizetion surface cover independent w Cover dependent nadir normalization classes bright waget medium dark veget dey waget soil shot s
366. tory containing prototype reflectance spectra of water soils vegetation asphalt etc Here the user can also put field measurements of surface reflectance spectra resampled for the appropriate sensor This is useful for inflight calibration or comparison of scene spectra with ground spectra CHAPTER 4 WORKFLOW 37 Input Image BSQ format Flat Terrain Rugged Terrain haze cloud water AOT water vapor Y cloud shadow Y value added Figure 4 10 Input output image files during ATCOR processing Finally the demo_data contains some demo imagery to be able to run ATCOR immediately atcor bn ASTER Landsat TM SPOT atm_lib ASTER Landsat TM SPOT spec lib ASTER Landsat TM SPOT demo data Figure 4 11 Directory structure of ATCOR 4 5 Convention for file names Although file names are arbitrary it is useful to agree on some conventions to facilitate the search of files especially concerning the extensions of file names Input images to ATCOR must have the band sequential format BSQ therefore it is recommended to employ the bsq as an extension e g magel bsq Then in this example the default output CHAPTER 4 WORKFLOW 38 alfalfa Reflectance Reflectance na 10 Es 2 0 E 1 0 Es 2 0 Wavelength zm Wavelength gm agricultural_soil Reflectance Reflectance OS 1 0 15 2 0 0 5 eo 13 20 Wavelength zm Wavelength gm Figure 4 12 T
367. ubarctic summer u2 2 92 cm mid latitude summer then wi uz u u2 u1 0 619 This manipulation can be performed in the SPECTRA module after pressing the button Mixing of Atmospheres There the user has to select two existing atmospheric files defines the weight w1 and assigns a file name to the new mixed LUTs This file can be loaded from the main ATCOR panel If no atmospheric water vapor information is available but lake temperature measurements exist the user can define an appropriate temperature offset kg to match the satellite derived temperature and the water body temperature The corresponding panel Surface Radiance to Temperature Conversion pops up when the SPECTRA module or the image processing options are entered CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 170 9 6 Metadata files geometry and calibration This section explains how the geometry and calibration information of various sensor specific meta data files has to be interpreted for use in ATCOR Besides the atmospheric LUTs for the nadir view there are files for off nadir view geometries covering tilt angles from 0 to 50 increment 10 and relative azimuth angles from 0 backscatter to 180 forward scatter with an increment of 30 the phase_fct bin files in the ATCOR directory A corresponding tilt azimuth angle interpolation of the LUT s is automatically done The meta data for a selected number of
368. ue green red NIR with a spatial resolution of 8 m and a panchromatic band 450 745 nm with a 1 5 m resolution The meta data file is in the XML format It is suggested to use the Read Sensor Meta Data button to extract the necessary information in the inn and cal file If the scene is named scene_xxx bsq then the xxx inn file generated by the meta file reader has to be renamed as scene_xxx inn before starting ATCOR CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 178 9 6 6 ALOS AVNIR 2 The ALOS satellite has a sun synchronous orbit of 690 km and among other instruments carries the AVNIR 2 optical payload AVNIR 2 has four spectral channels blue green red NIR with a nadir resolution of 10 m The instrument has a 44 across track tilt capability Different metafiles are available one is in the DIMAP format It contains the relevant geometric and radiometric parame ters The convention for the tilt and orientation angles is similar to SPOT compare Fig 9 10 i e a tilt left with respect to flight direction is coded as L tilt angle gt 0 a tilt right is coded as R tilt angle lt 0 The radiometric calibration coefficients are given in the unit mWm sr inm thus they have to be multiplied with 0 1 to convert them into the unit mWem sr yum used by ATCOR 9 6 7 Ikonos Ikonos metadata files look like po_3964_metadata txt where the po indicate
369. ue to red part of the spectrum channels in this region are checked and assigned to the class saturated false color coded red in the _out_hcw bsq file However the _atm log file contains the percentage of saturated pixels for each channel Cloud over land Pixels must satisfy the conditions p blue gt Te and p red gt 0 15 and p NIR p red lt 2 and p NIR gt 0 8 p red and p NIR p SWIR1 gt 1 and NDSI lt 0 7 or DN blue gt Tsaturation 10 51 where p blue is the apparent reflectance in a blue band T is the cloud threshold as defined in the preference parameter file and DN blue is the corresponding digital number If no blue band is available a green band around 550 nm is taken as a substitute If no green band exists a red band around 650 nm is taken NDSI is the normalized difference snow index p green p SWIR1 p green p SWIR1 NDSI 10 52 Note that saturated pixels in visible bands are automatically counted as cloud although they might be something else e g snow or a specular reflection from a surface If a thermal band exists the following cloud criterion must also be fulfilled 1 p SWIR1 Te lt 225 Kelvin and exclude Ty gt 300 Kelvin 10 53 where Jp is the at sensor blackbody temperature in the selected thermal band Cloud over water The following criteria have to be fulfilled 0 20 lt p blue lt 0 40 p green lt
370. unction i e the file smile_poly_ord4 dat in the corresponding sensor folder see chapter 4 7 Due to the smile shift the wavelength values of a spectral channel vary slightly in across track direction The smile interpolation function allows the specification of a common center wavelength for each channel Then for each channel all pixel reflectances are interpolated to this new reference wavelength Since the smile shift between adjacent bands does not vary significantly a linear interpolation can be applied If A i denotes the center wavelength of band i and column j and p z the surface reflectance of a column j pixel then the new interpolated CHAPTER 5 DESCRIPTION OF MODULES 104 reflectance is new XIV Ares 2 Aj 2 pj i 1 Pj i 1 Pj 2 Aref 1 Pj 1 T AUDA ee 5 5 where Are f z is the user defined reference center wavelength for band i There are three options for the reference wavelength grid 1 use wavelength corresponding to the center of the detector array 2 use average wavelength over all detector columns per band 3 use nominal wavelength specified in the ENVI header of the reflectance cube This tool is available in the interactive mode main menu then Filter then Spectral Smile Interpolation Image Cube and in the batch mode smile_interp3_batch see chapter 6 3 ALBA A Spectral Smile Interpolation Satellite ATCOR The Spectral Cube is I
371. ure 5 22 DEM Preparation Inputs Input DEM file Input DEM typically large than image Input Image file This is the target image georeferenced The DEM is automatically resized to its dimensions Window Diameter for DEM Processor Size of the kernel in number of pixels the slope and aspect and sky view side outputs are calculated using this kernel size Options The writing of side outputs i e slp and asp and sky may be triggered by deselecting these options Outputs An ATCOR elevation file _ele bsq is written of the same size and location as the reference Image Optionally files of slope and aspect and sky view are created same size as DEM Attention Both the DEM and the image should be represented in the same coordinate system and cover the same area If the DEM is too small the remaining parts of the image are filled with a zero value 5 3 3 Slope Aspect Slope and aspect are to be calculated from the DEM before ATCOR is run for rugged terrain This menu function as depicted in Fig 5 23 allows to calculate both layers in one step Inputs Input DEM file The standard DEM file used for atmospheric correction This DEM should be in meters Output file names The names for the output files are entered automatically and can t be changed as ATCOR asks these files to be named exactly according to the conventions CHAPTER 5 DESCRIPTION OF MODULES 70 AAA Slope and Aspect Calculation V 2 0 DEM File may have 16 o
372. ut file to be created logfile name of log file to be used or created elefile input elevation file for at_prepele factor factor for elevation data processing NOTE files names other than the input file may be set to dum in order to process default file names After execution the idl session quits and throws an error status of 1 if an error occurred during processing ATTENTION These routines are overwriting existing outputs 6 2 Using the batch mode from within IDL ATCOR can process scenes in batch mode For large scenes the tiling option is also available in batch mode which splits a big scene into a number of smaller sub scenes processes the sub scenes and finally merges them into one file A prerequite for the tiling is that enough IDL memory is available to keep one image channel and the sub scene channel in memory Also note that a full IDL developer license is required in order to make use of the batch commands on the IDL prompt The batch mode can be accessed after the processing parameters have been specified in the inter active graphical user interface GUI panel i e after the SPECTRA module has been accessed or after one of the image processing options has been selected Thus the GUI panel creates an inn file containing all input parameters The batch mode can be started after quitting the interactive session using the same IDL window It can also be started in a new IDL session after typing atcor
373. viewing angle along track 01 and across track 02 So the final viewing angle is 0 4 0 03 The satellite direction is specified in the parameter satellite azimuth The meta data can also be read using the ATCOR main panel File Read Sensor Meta Data which creates the corresponding cal and inn files using some default values for the latter file 9 6 17 Pleiades The multispectral Pleiades sensor has four bands blue green red NIR with a spatial resolution of 2m Pleiades 1 was launched on December 16 2011 The mean orbit altitude is 694 km the swath is 20 km the tilt angle up to 50 Data encoding is 12 bits pixel and the sensor has adjustable gain settings documented in the metafile for each scene The radiometric bias and gain are defined as L bias DN gain with the radiance unit Wm sr ym therefore the corresponding ATCOR offset cy and slope c values are co 0 1 bias 9 17 However the nomianl bias is 0 c 0 1 gain 9 18 Attention Pleiades imagery is usually distributed with the band sequence red green blue NIR and for ATCOR the band sequence should be re arranaged as blue green red NIR Chapter 10 Theoretical Background Standard books on optical remote sensing contain an extensive presentation on sensors spectral signatures and atmospheric effects where the interested reader is referred to Slater 1980 94 Asrar 1989 4 Schowengerdt 2007 91
374. w or low local sun elevations 10 1 4 Spectral solar flux reflected surface radiance The spectral solar fluxes on the ground can be calculated by setting the parameter zrrad0 1 in the nn file or using the graphical user interface The fluxes depend on solar geometry terrain elevation topography and atmospheric conditions All fluxes and the surface reflected radiance of this section are evaluated for the current earth sun distance For a flat terrain ATCOR provides spectra of the direct diffuse and global flux for the selected visibility water vapor In case of variable visibility water vapor the spectra are calculated for the average scene visibility water vapor The direct flux is just the beam irradiance on the ground times the cosine of the local solar zenith angle The diffuse flux spectrum Eg is evaluated for a surface reflectance of p 0 and the global flux for p 0 15 i e Ey Lair Eaif 0 1 s 0 15 where s is the spherical albedo The spectral band index is omitted for brevity For a flat terrain these fluxes are provided in the directory of the input file e g scene bsq e the direct spectral flux on the ground scene_edir dat e the diffuse spectral flux on the ground scene_edif dat for surface reflectance p 0 e the global spectral flux on the ground scene_eglo dat for a typical average surface reflectance p 0 15 These spectra will already give a realistic descrip
375. which are removed in the at surface quantities apart from small residual effects The bottom graphic presents the corresponding surface emissivity spectrum CHAPTER 4 WORKFLOW 47 20 15 5 28 d E TOO g 26 i a o esp me El E 2 a 22 black ns 4 E black at sensor Tbb rey gt ot surface gs a 2U qrey atosuriace Thb 2 0 18 8 3 10 11 12 13 E 3 10 11 12 13 Wavelength pam Wavelength m 1 00 O99 gt 098 E a E W oo 0 56 055 Ej 3 10 11 12 13 Wavelength pam Figure 4 17 Comparison of radiance and temperature at sensor and at surface level CHAPTER 4 WORKFLOW 48 4 10 External water vapor map Sometimes it is convenient to use an external water vapor map even if this map could be de rived from the scene If the scene is named scene bsq then the external map should be named scene_wv bsq and it must have the same number of image lines and pixels per line as the scene If this file is available in the same directory as the scene it will be automatically used during the processing and the usage is also documented in the log file scene_atm log Note this feature is only supported for instruments that are able to retrieve the water vapor column with the intrinsic channels because the prerequisite is the availability of the corresponding LUTs 4 11 External float illumination file and de shadowing If the scene is processed with a DEM the additional files of slope aspect and
376. x calculation used for flat terrain ignored for rugged terrain line 26 20 0 0 50 0 65 15 0 6 3 t air z0_ref teradient p_wv zh_pwv see chapter 7 t air air temperature Celsius at elevation z0_ref z0_ref reference elevation for t air km asl tgradient air temperature gradients Celsius per 100 m height p wv mb or hPa default water vapor partial pressure at z0_ref zh_pwv km scale height of water vapor exponential decrease falls to 1 e value Parameters for the net flux calculation rugged terrain ksolflux gt 0 These are dummy values not used if ksolflux 0 or for a flat terrain line 27 2 2 ihot_mask ihot_dynr parameters for haze correction ihot_mask 1 small area haze mask ihot_mask 2 large area haze mask ihot_dynr 1 thin to medium haze levels are corrected ihot_dynr 2 thin to thick haze levels are corrected line 28 2 0 500 0 12 0 08 1 iclshad mask thr_shad phi_unscl_max phi_scl_min istretch_type Parameters for correction of cloud building shadow effects if icl shadow gt 0 Default values are put in this line even if icl_shadow 0 iclshad_mask 1 2 3 small medium large cloud shadow mask thr_shad 0 500 threshold for core shadow areas 999 means threshold is calculated from image histogram phi_unscl_max max of unscaled shadow function Par see chapters 2 4 10 5 6 phi_sclmin min of scaled shadow function 7 see chapters 2 4 10 5 6
377. y of the atmospheric correction before processing the image data For that purpose the SPECTRA module should be used where the surface reflectance of small CHAPTER 4 WORKFLOW 34 user defined boxes can be evaluated and compared with library spectra compare chapter 5 4 6 In case of calibration problems the spectral calibration module available from the Tools button of Fig 4 1 and the radiometric inflight calibration may be employed before finally processing the image data The AEROSOL TYPE button provides an estimate for the recommended aerosol type e g rural maritime urban derived from the scene This module also provides a visibility value for each aerosol type based on reference pixels dark vegetation in the scene The VISIB ESTIMATE button provides a visibility value for the selected aerosol type by checking dark scene pixels in the red band vegetation water and NIR band water It is assumed that the lowest reflectance in the red band is 0 01 1 percent and 0 0 in the NIR band Therefore the obtained visibility value usually can be considered as a lower bound The higher visibility value of AEROSOL TYPE and VISIB ESTIMATE is recommended as a start visibility for the SPECTRA module The Inflight Calibration is described in chapter 5 4 9 This module is usually not required for first use of the software The WATER VAPOR button can be used to test the appropriate band combinations f
378. ype Three options are available for pushbroom spectral polishing Spectral Polishing is done in spectral dimension only one adjacent band on each spectral side is taken for residual calculation Spatial Only the spatial filter is applied use this option for destriping of imagery 2D Filter Do both dimensions spatial spectral simultaneously the filter size only applies to the spatial dimension however Type of Correction Function For each detector pixel correction parameters are generated Gain and Offset Calculate average residual gain and offset for each pixel and apply them as correction function Gain Only Constrain the residual gain to an offset of 0 this is the typical situation Output A cube containing the spectrally filtered copy of the original image data cube is created As a side output a gain file is written name _gain bsq containing the following three layers 1 offset of linear recalibration offset function 2 gain of linear recalibration offset function 3 gain of linear recalibration offset function if function is constrained to zero offset such that the corrected spectral band is Lyotisn Lori Lof fs gain Lori smooth 5 6 7 Spectral Smile Interpolation For sensors affected by spectral smile the surface reflectance cube is calculated accounting for the smile shift function in the column across track direction of the detector array The smile shift is specified as a 4th order polynomial f

PDF Manual ATCOR3

Contents

Download Pdf Manuals

Related Search

Related Contents