1i.atcorr(1) Grass User's Manual i.atcorr(1)
2
6 i.atcorr - Performs atmospheric correction using the 6S algorithm.
7 6S - Second Simulation of Satellite Signal in the Solar Spectrum.
8
10 imagery, atmospheric correction, radiometric conversion, radiance,
11 reflectance, satellite
12
14 i.atcorr
15 i.atcorr --help
16 i.atcorr [-irab] input=name [range=min,max] [elevation=name] [vis‐
17 ibility=name] parameters=name output=name [rescale=min,max]
18 [--overwrite] [--help] [--verbose] [--quiet] [--ui]
19 Flags:
21 -i
22 Output raster map as integer
23 -r
25 Input raster map converted to reflectance (default is radiance)
26 -a
28 Input from ETM+ image taken after July 1, 2000
29 -b
31 Input from ETM+ image taken before July 1, 2000
32 --overwrite
34 Allow output files to overwrite existing files
35 --help
37 Print usage summary
38 --verbose
40 Verbose module output
41 --quiet
43 Quiet module output
44 --ui
46 Force launching GUI dialog
47 Parameters:
49 input=name [required]
50 Name of input raster map
51 range=min,max
53 Input range
54 Default: 0,255
55 elevation=name
57 Name of input elevation raster map (in m)
58 visibility=name
60 Name of input visibility raster map (in km)
61 parameters=name [required]
63 Name of input text file with 6S parameters
64 output=name [required]
66 Name for output raster map
67 rescale=min,max
69 Rescale output raster map
70 Default: 0,255
71
73 i.atcorr performs atmospheric correction on the input raster map using
74 the 6S algorithm (Second Simulation of Satellite Signal in the Solar
75 Spectrum). A detailed algorithm description is available at the Land
76 Surface Reflectance Science Computing Facility website.
77 Important: Current region settings are ignored! The region is adjusted
79 to cover the input raster map before the atmospheric correction is per‐
80 formed. The previous settings are restored afterwards.
81 If the -r flag is used, the input raster map is treated as reflectance.
83 Otherwise, the input raster map is treated as radiance values and it is
84 converted to reflectance at the i.atcorr runtime. The output data are
85 always reflectance.
86 The satellite overpass time has to be specified in Greenwich Mean Time
88 (GMT).
89 An example of the 6S parameters could be:
91 8 - geometrical conditions=Landsat ETM+
92 2 19 13.00 -47.410 -20.234 - month day hh.ddd longitude latitude ("hh.ddd" is in decimal hours GMT)
93 1 - atmospheric model=tropical
94 1 - aerosols model=continental
95 15 - visibility [km] (aerosol model concentration)
96 -0.600 - mean target elevation above sea level [km] (here 600 m asl)
97 -1000 - sensor height (here, sensor on board a satellite)
98 64 - 4th band of ETM+ Landsat 7
99 If the position is not available in longitude-latitude (WGS84), the
100 m.proj conversion module can be used to reproject from a different ref‐
101 erence system.
102
104 A. Geometrical conditions
105 Code Description Details
106 1 meteosat observation enter month,day,decimal hour (universal time-hh.ddd) n. of
108 column,n. of line. (full scale 5000*2500)
109 2 goes east observation enter month,day,decimal hour (universal time-hh.ddd) n. of
111 column,n. of line. (full scale 17000*12000)c
112 3 goes west observation enter month,day,decimal hour (universal time-hh.ddd) n. of
114 column,n. of line. (full scale 17000*12000)
115 4 avhrr (PM noaa) enter month,day,decimal hour (universal time-hh.ddd) n. of
117 column(1-2048),xlonan,hna give long.(xlonan) and overpass
118 hour (hna) at the ascendant node at equator
119 5 avhrr (AM noaa) enter month,day,decimal hour (universal time-hh.ddd) n. of
121 column(1-2048),xlonan,hna give long.(xlonan) and overpass
122 hour (hna) at the ascendant node at equator
123 6 hrv (spot) enter month,day,hh.ddd,long.,lat. *
125 7 tm (landsat) enter month,day,hh.ddd,long.,lat. *
127 8 etm+ (landsat7) enter month,day,hh.ddd,long.,lat. *
129 9 liss (IRS 1C) enter month,day,hh.ddd,long.,lat. *
131 10 aster enter month,day,hh.ddd,long.,lat. *
133 11 avnir enter month,day,hh.ddd,long.,lat. *
137 12 ikonos enter month,day,hh.ddd,long.,lat. *
139 13 RapidEye enter month,day,hh.ddd,long.,lat. *
141 14 VGT1 (SPOT4) enter month,day,hh.ddd,long.,lat. *
143 15 VGT2 (SPOT5) enter month,day,hh.ddd,long.,lat. *
145 16 WorldView 2 enter month,day,hh.ddd,long.,lat. *
147 17 QuickBird enter month,day,hh.ddd,long.,lat. *
149 18 LandSat 8 enter month,day,hh.ddd,long.,lat. *
151 19 Geoeye 1 enter month,day,hh.ddd,long.,lat. *
153 20 Spot6 enter month,day,hh.ddd,long.,lat. *
155 21 Spot7 enter month,day,hh.ddd,long.,lat. *
157 22 Pleiades1A enter month,day,hh.ddd,long.,lat. *
159 23 Pleiades1B enter month,day,hh.ddd,long.,lat. *
161 24 Worldview3 enter month,day,hh.ddd,long.,lat. *
163 25 Sentinel-2A enter month,day,hh.ddd,long.,lat. *
165 26 Sentinel-2B enter month,day,hh.ddd,long.,lat. *
167 27 PlanetScope 0c 0d enter month,day,hh.ddd,long.,lat. *
169 28 PlanetScope 0e enter month,day,hh.ddd,long.,lat. *
171 29 PlanetScope 0f 10 enter month,day,hh.ddd,long.,lat. *
173 NOTE: for HRV, TM, ETM+, LISS and ASTER experiments, longitude and lat‐
176 itude are the coordinates of the scene center. Latitude must be > 0 for
177 northern hemisphere and < 0 for southern. Longitude must be > 0 for
178 eastern hemisphere and < 0 for western.
179 B. Atmospheric model
181 Code Meaning
182 0 no gaseous absorption
184 1 tropical
186 2 midlatitude summer
188 3 midlatitude winter
190 4 subarctic summer
192 5 subarctic winter
194 6 us standard 62
196 7 Define your own atmospheric model as a set of the following
204 5 parameters per each measurement: altitude [km] pressure
205 [mb] temperature [k] h2o density [g/m3] o3 density [g/m3]
206 For example: there is one radiosonde measurement for each
207 altitude of 0-25km at a step of 1km, one measurment for each
208 altitude of 25-50km at a step of 5km, and two single mea‐
209 surements for altitudes 70km and 100km. This makes 34 mea‐
210 surments. In that case, there are 34*5 values to input.
211 8 Define your own atmospheric model providing values of the
213 water vapor and ozone content: uw [g/cm2] uo3 [cm-atm] The
214 profile is taken from us62.
215 C. Aerosols model
218 Code Meaning Details
219 0 no aerosols
221 1 continental model
223 2 maritime model
225 3 urban model
227 4 shettle model for background desert aerosol
229 5 biomass burning
231 6 stratospheric model
233 7 define your own model Enter the volumic percentage of each component: c(1) = volu‐
235 mic % of dust-like c(2) = volumic % of water-soluble c(3) =
236 volumic % of oceanic c(4) = volumic % of soot All values
237 should be between 0 and 1.
238 8 define your own model Size distribution function: Multimodal Log Normal (up to 4
240 modes).
241 9 define your own model Size distribution function: Modified gamma.
243 10 define your own model Size distribution function: Junge Power-Law.
245 11 define your own model Sun-photometer measurements, 50 values max, entered as: r
247 and d V / d (logr) where r is the radius [micron], V is the
248 volume, d V / d (logr) [cm3/cm2/micron]. Followed by: nr
249 and ni for each wavelength where nr and ni are respectively
250 the real and imaginary part of the refractive index.
251 D. Aerosol concentration model (visibility)
254 If you have an estimate of the meteorological parameter visibility v,
255 enter directly the value of v [km] (the aerosol optical depth (AOD)
256 will be computed from a standard aerosol profile).
257 If you have an estimate of aerosol optical depth, enter 0 for the visi‐
259 bility and in a following line enter the aerosol optical depth at 550nm
260 (iaer means ’i’ for input and ’aer’ for aerosol), for example:
261 0 - visibility
262 0.112 - aerosol optical depth at 550 nm
263 NOTE: if iaer is 0, enter -1 for visibility.
265 NOTE: if a visibility map is provided, these parameters are ignored.
267 E. Target altitude (xps), sensor platform (xpp)
269 Target altitude (xps, in negative [km]): xps >= 0 means the target is
270 at the sea level.
271 otherwise xps expresses the altitude of the target (e.g., mean eleva‐
272 tion) in [km], given as negative value
273 Sensor platform (xpp, in negative [km] or -1000):
274 xpp = -1000 means that the sensor is on board a satellite.
275 xpp = 0 means that the sensor is at the ground level.
276 -100 < xpp < 0 defines the altitude of the sensor expressed in [km];
277 this altitude is given relative to the target altitude as negative
278 value.
279 For aircraft simulations only (xpp is neither equal to 0 nor equal to
281 -1000): puw,po3 (water vapor content,ozone content between the aircraft
282 and the surface)
283 taerp (the aerosol optical thickness at 550nm between the aircraft and
284 the surface)
285 If these data are not available, enter negative values for all of them.
287 puw,po3 will then be interpolated from the us62 standard profile
288 according to the values at the ground level; taerp will be computed
289 according to a 2 km exponential profile for aerosol.
290 F. Sensor band
292 There are two possibilities: either define your own spectral conditions
293 (codes -2, -1, 0, or 1) or choose a code indicating the band of one of
294 the pre-defined satellites.
295 Define your own spectral conditions:
297 Code Meaning
299 -2 Enter wlinf, wlsup. The filter function will be equal to 1
301 over the whole band (as iwave=0) but step by step output
302 will be printed.
303 -1 Enter wl (monochr. cond, gaseous absorption is included).
305 0 Enter wlinf, wlsup. The filter function will be equal to 1
307 over the whole band.
308 1 Enter wlinf, wlsup and user’s filter function s (lambda) by
310 step of 0.0025 micrometer.
311 Pre-defined satellite bands:
314 Code Band name (peak response)
316 2 meteosat vis band (0.350-1.110)
318 3 goes east band vis (0.490-0.900)
320 4 goes west band vis (0.490-0.900)
322 5 avhrr (noaa6) band 1 (0.550-0.750)
324 6 avhrr (noaa6) band 2 (0.690-1.120)
326 7 avhrr (noaa7) band 1 (0.500-0.800)
328 8 avhrr (noaa7) band 2 (0.640-1.170)
330 9 avhrr (noaa8) band 1 (0.540-1.010)
332 10 avhrr (noaa8) band 2 (0.680-1.120)
334 11 avhrr (noaa9) band 1 (0.530-0.810)
336 12 avhrr (noaa9) band 1 (0.680-1.170)
338 13 avhrr (noaa10) band 1 (0.530-0.780)
342 14 avhrr (noaa10) band 2 (0.600-1.190)
344 15 avhrr (noaa11) band 1 (0.540-0.820)
346 16 avhrr (noaa11) band 2 (0.600-1.120)
348 17 hrv1 (spot1) band 1 (0.470-0.650)
350 18 hrv1 (spot1) band 2 (0.600-0.720)
352 19 hrv1 (spot1) band 3 (0.730-0.930)
354 20 hrv1 (spot1) band pan (0.470-0.790)
356 21 hrv2 (spot1) band 1 (0.470-0.650)
358 22 hrv2 (spot1) band 2 (0.590-0.730)
360 23 hrv2 (spot1) band 3 (0.740-0.940)
362 24 hrv2 (spot1) band pan (0.470-0.790)
364 25 tm (landsat5) band 1 (0.430-0.560)
366 26 tm (landsat5) band 2 (0.500-0.650)
368 27 tm (landsat5) band 3 (0.580-0.740)
370 28 tm (landsat5) band 4 (0.730-0.950)
372 29 tm (landsat5) band 5 (1.5025-1.890)
374 30 tm (landsat5) band 7 (1.950-2.410)
376 31 mss (landsat5) band 1 (0.475-0.640)
378 32 mss (landsat5) band 2 (0.580-0.750)
380 33 mss (landsat5) band 3 (0.655-0.855)
382 34 mss (landsat5) band 4 (0.785-1.100)
384 35 MAS (ER2) band 1 (0.5025-0.5875)
386 36 MAS (ER2) band 2 (0.6075-0.7000)
388 37 MAS (ER2) band 3 (0.8300-0.9125)
390 38 MAS (ER2) band 4 (0.9000-0.9975)
392 39 MAS (ER2) band 5 (1.8200-1.9575)
394 40 MAS (ER2) band 6 (2.0950-2.1925)
396 41 MAS (ER2) band 7 (3.5800-3.8700)
398 42 MODIS band 1 (0.6100-0.6850)
400 43 MODIS band 2 (0.8200-0.9025)
402 44 MODIS band 3 (0.4500-0.4825)
404 45 MODIS band 4 (0.5400-0.5700)
406 46 MODIS band 5 (1.2150-1.2700)
408 47 MODIS band 6 (1.6000-1.6650)
411 48 MODIS band 7 (2.0575-2.1825)
413 49 avhrr (noaa12) band 1 (0.500-1.000)
415 50 avhrr (noaa12) band 2 (0.650-1.120)
417 51 avhrr (noaa14) band 1 (0.500-1.110)
419 52 avhrr (noaa14) band 2 (0.680-1.100)
421 53 POLDER band 1 (0.4125-0.4775)
423 54 POLDER band 2 (non polar) (0.4100-0.5225)
425 55 POLDER band 3 (non polar) (0.5325-0.5950)
427 56 POLDER band 4 P1 (0.6300-0.7025)
429 57 POLDER band 5 (non polar) (0.7450-0.7800)
431 58 POLDER band 6 (non polar) (0.7000-0.8300)
433 59 POLDER band 7 P1 (0.8100-0.9200)
435 60 POLDER band 8 (non polar) (0.8650-0.9400)
437 61 etm+ (landsat7) band 1 blue (435nm - 517nm)
439 62 etm+ (landsat7) band 2 green (508nm - 617nm)
441 63 etm+ (landsat7) band 3 red (625nm - 702nm)
443 64 etm+ (landsat7) band 4 NIR (753nm - 910nm)
445 65 etm+ (landsat7) band 5 SWIR (1520nm - 1785nm)
447 66 etm+ (landsat7) band 7 SWIR (2028nm - 2375nm)
449 67 etm+ (landsat7) band 8 PAN (505nm - 917nm)
451 68 liss (IRC 1C) band 2 (0.502-0.620)
453 69 liss (IRC 1C) band 3 (0.612-0.700)
455 70 liss (IRC 1C) band 4 (0.752-0.880)
457 71 liss (IRC 1C) band 5 (1.452-1.760)
459 72 aster band 1 (0.480-0.645)
461 73 aster band 2 (0.588-0.733)
463 74 aster band 3N (0.723-0.913)
465 75 aster band 4 (1.530-1.750)
467 76 aster band 5 (2.103-2.285)
469 77 aster band 6 (2.105-2.298)
471 78 aster band 7 (2.200-2.393)
473 79 aster band 8 (2.248-2.475)
475 80 aster band 9 (2.295-2.538)
477 81 avnir band 1 (408nm - 517nm)
480 82 avnir band 2 (503nm - 612nm)
482 83 avnir band 3 (583nm - 717nm)
484 84 avnir band 4 (735nm - 922nm)
486 85 Ikonos Green band (408nm - 642nm)
488 86 Ikonos Red band (448nm - 715nm)
490 87 Ikonos NIR band (575nm - 787nm)
492 88 RapidEye Blue band (440nm - 512nm)
494 89 RapidEye Green band (515nm - 592nm)
496 90 RapidEye Red band (628nm - 687nm)
498 91 RapidEye Red edge band (685nm - 735nm)
500 92 RapidEye NIR band (750nm - 860nm)
502 93 VGT1 (SPOT4) band 0 (420nm - 497nm)
504 94 VGT1 (SPOT4) band 2 (603nm - 747nm)
506 95 VGT1 (SPOT4) band 3 (740nm - 942nm)
508 96 VGT1 (SPOT4) MIR band (1540nm - 1777nm)
510 97 VGT2 (SPOT5) band 0 (423nm - 492nm)
512 98 VGT2 (SPOT5) band 2 (600nm - 737nm)
514 99 VGT2 (SPOT5) band 3 (745nm - 945nm)
516 100 VGT2 (SPOT5) MIR band (1523nm - 1757nm)
518 101 WorldView2 Panchromatic band (448nm - 812nm)
520 102 WorldView2 Coastal Blue band (395nm - 457nm)
522 103 WorldView2 Blue band (440nm - 517nm)
524 104 WorldView2 Green band (503nm - 587nm)
526 105 WorldView2 Yellow band (583nm - 632nm)
528 106 WorldView2 Red band (623nm - 695nm)
530 107 WorldView2 Red edge band (698nm - 750nm)
532 108 WorldView2 NIR1 band (760nm - 905nm)
534 109 WorldView2 NIR2 band (853nm - 1047nm)
536 110 QuickBird Panchromatic band (385nm - 1060nm)
538 111 QuickBird Blue band (420nm - 585nm)
540 112 QuickBird Green band (448nm - 682nm)
542 113 QuickBird Red band (560nm - 747nm)
544 114 QuickBird NIR1 band (650nm - 935nm)
546 115 Landsat 8 Coastal aerosol band (433nm - 455nm)
549 116 Landsat 8 Blue band (448nm - 515nm)
551 117 Landsat 8 Green band (525nm - 595nm)
553 118 Landsat 8 Red band (633nm - 677nm)
555 119 Landsat 8 Panchromatic band (498nm - 682nm)
557 120 Landsat 8 NIR band (845nm - 885nm)
559 121 Landsat 8 Cirrus band (1355nm - 1390nm)
561 122 Landsat 8 SWIR1 band (1540nm - 1672nm)
563 123 Landsat 8 SWIR2 band (2073nm - 2322nm)
565 124 GeoEye 1 Panchromatic band (448nm - 812nm)
567 125 GeoEye 1 Blue band (443nm - 525nm)
569 126 GeoEye 1 Green band (503nm - 587nm)
571 127 GeoEye 1 Red band (653nm - 697nm)
573 128 GeoEye 1 NIR band (770nm - 932nm)
575 129 Spot6 Blue band (440nm - 532nm)
577 130 Spot6 Green band (515nm - 600nm)
579 131 Spot6 Red band (610nm - 710nm)
581 132 Spot6 NIR band (738nm - 897nm)
583 133 Spot6 Pan band (438nm - 760nm)
585 134 Spot7 Blue band (445nm - 532nm)
587 135 Spot7 Green band (525nm - 607nm)
589 136 Spot7 Red band (610nm - 727nm)
591 137 Spot7 NIR band (745nm - 902nm)
593 138 Spot7 Pan band (443nm - 760nm)
595 139 Pleiades1A Blue band (433nm - 560nm)
597 140 Pleiades1A Green band (500nm - 617nm)
599 141 Pleiades1A Red band (590nm - 722nm)
601 142 Pleiades1A NIR band (740nm - 945nm)
603 143 Pleiades1A Pan band (460nm - 845nm)
605 144 Pleiades1B Blue band 438nm - 560nm)
607 145 Pleiades1B Green band (498nm - 615nm)
609 146 Pleiades1B Red band (608nm - 727nm)
611 147 Pleiades1B NIR band (750nm - 945nm)
613 148 Pleiades1B Pan band (460nm - 845nm)
615 149 Worldview3 Pan band (445nm - 812nm)
618 150 Worldview3 Coastal blue band (395nm - 455nm)
620 151 Worldview3 Blue band (443nm - 517nm)
622 152 Worldview3 Green band (508nm - 587nm)
624 153 Worldview3 Yellow band (580nm - 630nm)
626 154 Worldview3 Red band 625nm - 697nm)
628 155 Worldview3 Red edge band (698nm - 752nm)
630 156 Worldview3 NIR1 band (760nm - 902nm)
632 157 Worldview3 NIR2 band (855nm - 1042nm)
634 158 Worldview3 SWIR1 band (1178nm - 1242nm)
636 159 Worldview3 SWIR2 band (1545nm - 1600nm)
638 160 Worldview3 SWIR3 band (1633nm - 1687nm)
640 161 Worldview3 SWIR4 band (1698nm - 1762nm)
642 162 Worldview3 SWIR5 band (2133nm - 2195nm)
644 163 Worldview3 SWIR6 band (2170nm - 2235nm)
646 164 Worldview3 SWIR7 band (2225nm - 2295nm)
648 165 Worldview3 SWIR8 band (2283nm - 2377nm)
650 166 Sentinel2A Coastal blue band B1 (430nm - 455nm)
652 167 Sentinel2A Blue band B2 (440nm - 530nm)
654 168 Sentinel2A Green band B3 (540nm - 580nm)
656 169 Sentinel2A Red band B4 (648nm - 682nm)
658 170 Sentinel2A Red edge band B5 (695nm - 712nm)
660 171 Sentinel2A Red edge band B6 (733nm - 747nm)
662 172 Sentinel2A Red edge band B7 (770nm - 795nm)
664 173 Sentinel2A NIR band B8 (775nm - 905nm)
666 174 Sentinel2A Red edge band B8A (850nm - 880nm)
668 175 Sentinel2A Water vapour band B9 (933nm - 957nm)
670 176 Sentinel2A SWIR Cirrus band B10 (1355nm - 1392nm)
672 177 Sentinel2A SWIR band B11 (1558nm - 1667nm)
674 178 Sentinel2A SWIR band B12 (2088nm - 2315nm)
676 179 Sentinel2B Coastal blue band B1 (430nm - 455nm)
678 180 Sentinel2B Blue band B2 (440nm - 530nm)
680 181 Sentinel2B Green band B3 (538nm - 580nm)
682 182 Sentinel2B Red band B4 (648nm - 682nm)
684 183 Sentinel2B Red edge band B5 (695nm - 712nm)
687 184 Sentinel2B Red edge band B6 (730nm - 747nm)
689 185 Sentinel2B Red edge band B7 (768nm - 792nm)
691 186 Sentinel2B NIR band B8 (778nm - 905nm)
693 187 Sentinel2B Red edge band B8A (850nm - 877nm)
695 188 Sentinel2B Water vapour band B9 (930nm - 955nm)
697 189 Sentinel2B SWIR Cirrus band B10 (1358nm - 1397nm)
699 190 Sentinel2B SWIR band B11 (1555nm - 1667nm)
701 191 Sentinel2B SWIR band B12 (2075nm - 2300nm)
703 192 PlanetScope 0c 0d Blue band B1 (440nm - 570nm)
705 193 PlanetScope 0c 0d Green band B2 (450nm - 690nm)
707 194 PlanetScope 0c 0d Red band B3 (460nm - 700nm)
709 195 PlanetScope 0c 0d NIR band B4 (770nm - 880nm)
711 196 PlanetScope 0e Blue band B1 (430nm - 700nm)
713 197 PlanetScope 0e Green band B2 (450nm - 700nm)
715 198 PlanetScope 0e Red band B3 (460nm - 700nm)
717 199 PlanetScope 0e NIR band B4 (760nm - 880nm)
719 200 PlanetScope 0f 10 Blue band B1 (450nm - 680nm)
721 201 PlanetScope 0f 10 Green band B2 (450nm - 680nm)
723 202 PlanetScope 0f 10 Red band B3 (450nm - 680nm)
725 203 PlanetScope 0f 10 NIR band B4 (760nm - 870nm)
727
730 Atmospheric correction of a Sentinel-2 band
731 This example illustrates how to perform atmospheric correction of a
732 Sentinel-2 scene in the North Carolina location.
733 Let’s assume that the Sentinel-2 L1C scene
735 S2A_OPER_PRD_MSIL1C_PDMC_20161029T092602_R054_V20161028T155402_20161028T155402
736 was downloaded and imported with region cropping (see r.import) into
737 the PERMANENT mapset of the North Carolina location. The computational
738 region was set to the extent of the elevation map in the North Carolina
739 dataset. Now, we have 13 individual bands (B01-B12) that we want to
740 apply the atmospheric correction to. The following steps are applied
741 to each band separately.
742 Create the parameters file for i.atcorr
744 In the first step we create a file containing the 6S parameters for a
746 particular scene and band. To create a 6S file, we need to obtain the
747 following information:
748 · geometrical conditions,
750 · moth, day, decimal hours in GMT, decimal longitude and latitude
752 of measurement,
753 · atmospheric model,
755 · aerosol model,
757 · visibility or aerosol optical depth,
759 · mean target elevation above sea level,
761 · sensor height and,
763 · sensor band.
765 1 Geometrical conditions
767 For Sentinel-2A, the geometrical conditions take the value 25 and for
769 Sentinel-2B, the geometrical conditions value is 26 (See table A). Our
770 scene comes from the Sentinel-2A mission (the file name begins with
771 S2A_...).
772 2 Day, time, longitude and latitude of measurement
774 Day and time of the measurement are hidden in the filename (i.e., the
776 second datum in the file name with format YYYYMMDDTHHMMSS), and are
777 also noted in the metadata file, which is included in the downloaded
778 scene (file with .xml extension). Our sample scene was taken on October
779 28th (20161028) at 15:54:02 (155402). Note that the time has to be
780 specified in decimal hours in Greenwich Mean Time (GMT). Luckily, the
781 time in the scene name is in GMT and we can convert it to decimal hours
782 as follows: 15 + 54/60 + 2/3600 = 15.901.
783 Longitude and latitude refer to the centre of the computational region
785 (which can be smaller than the scene), and must be in WGS84 decimal
786 coordinates. To obtain the coordinates of the centre, we can run:
787 g.region -bg
788 The longitude and latitude of the centre are stored in ll_clon and
790 ll_clat. In our case, ll_clon=-78.691 and ll_clat=35.749.
791 3 Atmospheric model
793 We can choose between various atmospheric models as defined at the
795 beginning of this manual. For North Carolina, we can choose 2 - midlat‐
796 itude summer.
797 4 Aerosol model
799 We can also choose between various aerosol models as defined at the
801 beginning of this manual. For North Carolina, we can choose 1 - conti‐
802 nental model.
803 5 Visibility or Aerosol Optical Depth
805 For Sentinel-2 scenes, the visibility is not measured, and therefore we
807 have to estimate the aerosol optical depth instead, e.g. from AERONET.
808 With a bit of luck, you can find a station nearby your location, which
809 measured the Aerosol Optical Depth at 500 nm at the same time as the
810 scene was taken. In our case, on 28th October 2016, the EPA-Res_Trian‐
811 gle_Pk station measured AOD = 0.07 (approximately).
812 6 Mean target elevation above sea level
814 Mean target elevation above sea level refers to the mean elevation of
816 the computational region. You can estimate it from the digital eleva‐
817 tion model, e.g. by running:
818 r.univar -g elevation
819 The mean elevation is stored in mean. In our case, mean=110. In the 6S
821 file it will be displayed in [-km], i.e., -0.110.
822 7 Sensor height
824 Since the sensor is on board a satellite, the sensor height will be set
826 to -1000.
827 8 Sensor band
829 The overview of satellite bands can be found in table F (see above).
831 For Sentinel-2A, the band numbers span from 166 to 178, and for Sen‐
832 tinel-2B, from 179 to 191.
833 Finally, here is what the 6S file would look like for Band 02 of our
835 scene. In order to use it in the i.atcorr module, we can save it in a
836 text file, for example params_B02.txt.
837 25
838 10 28 15.901 -78.691 35.749
839 2
840 1
841 0
842 0.07
843 -0.110
844 -1000
845 167
846 Compute atmospheric correction
848 In the next step we run i.atcorr for the selected band B02 of our Sen‐
850 tinel 2 scene. We have to specify the following parameters:
851 · input = raster band to be processed,
853 · parameters = path to 6S file created in the previous step (we
855 could also enter the values directly),
856 · output = name for the output corrected raster band,
858 · range = from 1 to the QUANTIFICATION_VALUE stored in the meta‐
860 data file. It is 10000 for both Sentinel-2A and Sentinel-2B.
861 · rescale = the output range of values for the corrected bands.
863 This is up to the user to choose, for example: 0-255, 0-1,
864 1-10000.
865 If the data is available, the following parameters can be specified as
867 well:
868 · elevation = raster of digital elevation model,
870 · visibility = raster of visibility model.
872 Finally, this is how the command would look like to apply atmospheric
874 correction to band B02:
875 i.atcorr input=B02 parameters=params_B02.txt output=B02.atcorr range=1,10000 rescale=0,255 elevation=elevation
876 To apply atmospheric correction to the remaining bands, only the last
878 line in the 6S parameters file (i.e., the sensor band) needs to be
879 changed. The other parameters will remain the same.
880 Figure: Sentinel-2A Band 02 with applied atmospheric correction (his‐
881 togram equalization grayscale color scheme)
882 Atmospheric correction of a Landsat-7 band
884 This example is also based on the North Carolina sample dataset (GMT -5
885 hours). First we set the computational region to the satellite map,
886 e.g. band 4:
887 g.region raster=lsat7_2002_40 -p
888 It is important to verify the available metadata for the sun position
890 which has to be defined for the atmospheric correction. An option is to
891 check the satellite overpass time with sun position as reported in the
892 metadata file (file copy; North Carolina sample dataset). In the case
893 of the North Carolina sample dataset, these values have been stored for
894 each channel and can be retrieved with:
895 r.info lsat7_2002_40
896 In this case, we have: SUN_AZIMUTH = 120.8810347, SUN_ELEVATION =
897 64.7730999.
898 If the sun position metadata are unavailable, we can also calculate
900 them from the overpass time as follows (r.sunmask uses SOLPOS):
901 r.sunmask -s elev=elevation out=dummy year=2002 month=5 day=24 hour=10 min=42 sec=7 timezone=-5
902 # .. reports: sun azimuth: 121.342461, sun angle above horz.(refraction corrected): 65.396652
903 If the overpass time is unknown, use the NASA LaRC Satellite Overpass
904 Predictor.
905 Convert digital numbers (DN) to radiance at top-of-atmosphere (TOA)
907 For Landsat and ASTER, the conversion can be conveniently done with
908 i.landsat.toar or i.aster.toar, respectively.
909 In case of different satellites, the conversion of DN (digital number =
911 pixel values) to radiance at top-of-atmosphere (TOA) can also be done
912 manually, using e.g. the formula:
913 # formula depends on satellite sensor, see respective metadata
914 Lλ = ((LMAXλ - LMINλ)/(QCALMAX-QCALMIN)) * (QCAL-QCALMIN) + LMINλ
915 where,
916 · Lλ = Spectral Radiance at the sensor’s aperture in
918 Watt/(meter squared * ster * µm), the apparent radiance as seen
919 by the satellite sensor;
920 · QCAL = the quantized calibrated pixel value in DN;
922 · LMINλ = the spectral radiance that is scaled to QCALMIN in
924 watts/(meter squared * ster * µm);
925 · LMAXλ = the spectral radiance that is scaled to QCALMAX in
927 watts/(meter squared * ster * µm);
928 · QCALMIN = the minimum quantized calibrated pixel value (corre‐
930 sponding to LMINλ) in DN;
931 · QCALMAX = the maximum quantized calibrated pixel value (corre‐
933 sponding to LMAXλ) in DN=255.
934 LMINλ and LMAXλ are the radiances related to the minimal and
935 maximal DN value, and they are reported in the metadata file of each
936 image. High gain or low gain is also reported in the metadata file of
937 each satellite image. For Landsat ETM+, the minimal DN value (QCALMIN)
938 is 1 (see Landsat handbook, chapter 11), and the maximal DN value
939 (QCALMAX) is 255. QCAL is the DN value for every separate pixel in the
940 Landsat image.
941 We extract the coefficients and apply them in order to obtain the radi‐
943 ance map:
944 CHAN=4
945 r.info lsat7_2002_${CHAN}0 -h | tr ’\n’ ’ ’ | sed ’s+ ++g’ | tr ’:’ ’\n’ | grep "LMIN_BAND${CHAN}\|LMAX_BAND${CHAN}"
946 LMAX_BAND4=241.100,p016r035_7x20020524.met
947 LMIN_BAND4=-5.100,p016r035_7x20020524.met
948 QCALMAX_BAND4=255.0,p016r035_7x20020524.met
949 QCALMIN_BAND4=1.0,p016r035_7x20020524.met
950 Conversion to radiance (this calculation is done for band 4, for the
951 other bands, the numbers will need to be replaced with their related
952 values):
953 r.mapcalc "lsat7_2002_40_rad = ((241.1 - (-5.1)) / (255.0 - 1.0)) * (lsat7_2002_40 - 1.0) + (-5.1)"
954 Again, the r.mapcalc calculation is only needed when working with
955 satellite data other than Landsat or ASTER.
956 Create the parameters file for i.atcorr
958 The underlying 6S model is parametrized through a control file, indi‐
959 cated with the parameters option. This is a text file defining geomet‐
960 rical and atmospherical conditions of the satellite overpass. Here we
961 create a control file icnd_lsat4.txt for band 4 (NIR), based on meta‐
962 data. For the overpass time, we need to define decimal hours: 10:42:07
963 NC local time = 10.70 decimal hours (decimal minutes: 42 * 100 / 60)
964 which is 15.70 GMT.
965 8 - geometrical conditions=Landsat ETM+
966 5 24 15.70 -78.691 35.749 - month day hh.ddd longitude latitude ("hh.ddd" is in GMT decimal hours)
967 2 - atmospheric model=midlatitude summer
968 1 - aerosols model=continental
969 50 - visibility [km] (aerosol model concentration)
970 -0.110 - mean target elevation above sea level [km]
971 -1000 - sensor on board a satellite
972 64 - 4th band of ETM+ Landsat 7
973 Finally, run the atmospheric correction (-r for reflectance input map;
974 -a for date > July 2000):
975 i.atcorr -r -a lsat7_2002_40_rad elevation=elevation parameters=icnd_lsat4.txt output=lsat7_2002_40_atcorr
976 Note that the altitude value from ’icnd_lsat4.txt’ file is read at the
977 beginning to compute the initial transform. Therefore, it is necessary
978 to provide a value that might be the mean value of the elevation model
979 (r.univar elevation). For the atmospheric correction per se, the eleva‐
980 tion values from the raster map are used.
981 Note that the process is computationally intensive. Note also, that
983 i.atcorr reports solar elevation angle above horizon rather than solar
984 zenith angle.
985
987 The influence and importance of the visibility value or map should be
988 explained, also how to obtain an estimate for either visibility or
989 aerosol optical depth at 550nm.
990
992 GRASS Wiki page about Atmospheric correction
993 i.aster.toar, i.landsat.toar, r.info, r.mapcalc, r.univar
995
997 · Vermote, E.F., Tanre, D., Deuze, J.L., Herman, M., and Mor‐
998 crette, J.J., 1997, Second simulation of the satellite signal
999 in the solar spectrum, 6S: An overview., IEEE Trans. Geosc. and
1000 Remote Sens. 35(3):675-686.
1001 · 6S Manual: PDF1, PDF2, and PDF3
1003 · RapidEye sensors have been provided by RapidEye AG, Germany
1005 · Barsi, J.A., Markham, B.L. and Pedelty, J.A., 2011, The opera‐
1007 tional land imager: spectral response and spectral uniformity.,
1008 Proc. SPIE 8153, 81530G; doi:10.1117/12.895438
1009
1011 Original version of the program for GRASS 5:
1012 Christo Zietsman, 13422863(at)sun.ac.za
1013 Code clean-up and port to GRASS 6.3, 15.12.2006:
1015 Yann Chemin, ychemin(at)gmail.com
1016 Documentation clean-up + IRS LISS sensor addition 5/2009:
1018 Markus Neteler, FEM, Italy
1019 ASTER sensor addition 7/2009:
1021 Michael Perdue, Canada
1022 AVNIR, IKONOS sensors addition 7/2010:
1024 Daniel Victoria, Anne Ghisla
1025 RapidEye sensors addition 11/2010:
1027 Peter Löwe, Anne Ghisla
1028 VGT1 and VGT2 sensors addition from 6SV-1.1 sources, addition 07/2011:
1030 Alfredo Alessandrini, Anne Ghisla
1031 Added Landsat 8 from NASA sources, addition 05/2014:
1033 Nikolaos Ves
1034 Geoeye1 addition 7/2015:
1036 Marco Vizzari
1037 Worldview3 addition 8/2016:
1039 Markus Neteler, mundialis.de, Germany
1040 Sentinel-2A addition 12/2016:
1042 Markus Neteler, mundialis.de, Germany
1043 Sentinel-2B addition 1/2018:
1045 Stefan Blumentrath, Zofie Cimburova, Norwegian Institute for Nature
1046 Research, NINA, Oslo, Norway
1047 Last changed: $Date: 2018-12-27 18:44:04 +0100 (Thu, 27 Dec 2018) $
1049
1051 Available at: i.atcorr source code (history)
1052 Main index | Imagery index | Topics index | Keywords index | Graphical
1054 index | Full index
1055 © 2003-2019 GRASS Development Team, GRASS GIS 7.6.0 Reference Manual
1057GRASS 7.6.0 i.atcorr(1)