i.atcorr(1grass)

๐Ÿ“ฆ
version 8.5.0 ยท 10 versions
, in alpine 3.24, ubuntu stonking

hey bestie ๐Ÿ’– welcome to the i.atcorr(1grass) page from the GRASS User's Manual. it's giving atmospheric correction realness, and we're gonna read it together like the iconic manual it is. no cap. โœจ

โœจ NAME โœจ

i.atcorr โ€” Performs atmospheric correction using the 6S algorithm. 6S - Second Simulation of Satellite Signal in the Solar Spectrum.

โœจ KEYWORDS โœจ

imagery, atmospheric correction, radiometric conversion, radiance, reflectance, satellite

โœจ SYNOPSIS โœจ

i.atcorr
i.atcorr --help
i.atcorr [-irab] input=name  [range=min,max]   [elevation=name]
[visibility=name]  parameters=name output=name  [rescale=min,max]
[--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

Flags

  • -i โ€” Output raster map as integer
  • -r โ€” Input raster map converted to reflectance (default is radiance)
  • -a โ€” Input from ETM+ image taken after July 1, 2000
  • -b โ€” Input from ETM+ image taken before July 1, 2000
  • --overwrite โ€” Allow output files to overwrite existing files
  • --help โ€” Print usage summary
  • --verbose โ€” Verbose module output
  • --quiet โ€” Quiet module output
  • --ui โ€” Force launching GUI dialog

Parameters

  • input=name [required] โ€” Name of input raster map
  • range=min,max โ€” Input range. Default: 0,255
  • elevation=name โ€” Name of input elevation raster map (in m)
  • visibility=name โ€” Name of input visibility raster map (in km)
  • parameters=name [required] โ€” Name of input text file with 6S parameters
  • output=name [required] โ€” Name for output raster map
  • rescale=min,max โ€” Rescale output raster map. Default: 0,255

โœจ DESCRIPTION โœจ

okay so i.atcorr does atmospheric correction on your input raster map using the 6S algorithm (Second Simulation of Satellite Signal in the Solar Spectrum). a detailed algorithm description is available at the Land Surface Reflectance Science Computing Facility website. ๐Ÿ’…

Important: Current region settings are ignored! The region is adjusted to cover the input raster map before the atmospheric correction is performed. The previous settings are restored afterwards.

If the -r flag is used, the input raster map is treated as reflectance. Otherwise, the input raster map is treated as radiance values and it is converted to reflectance at the i.atcorr runtime. The output data are always reflectance.

The satellite overpass time has to be specified in Greenwich Mean Time (GMT).

An example of the 6S parameters could be:

8                            - geometrical conditions=Landsat ETM+
2 19 13.00 -47.410 -20.234   - month day hh.ddd longitude latitude ("hh.ddd" is in decimal hours GMT)
1                            - atmospheric model=tropical
1                            - aerosols model=continental
15                           - visibility [km] (aerosol model concentration)
-0.600                       - mean target elevation above sea level [km] (here 600 m asl)
-1000                        - sensor height (here, sensor on board a satellite)
64                           - 4th band of ETM+ Landsat 7

If the position is not available in longitude-latitude (WGS84), the m.proj conversion module can be used to reproject from a different reference system.

โœจ 6S CODE PARAMETER CHOICES โœจ

the original has a big table with columns Code, Description, Details. we're turning it into a list, but every cell is here, exactly. no skips, no cuts. periodt. ๐Ÿ’…

A. Geometrical conditions

pick your fighter from this absolute roster of geometrical condition codes. ๐Ÿ‘‘

  • 1 โ€” meteosat observation โ€” enter month,day,decimal hour (universal time-hh.ddd) n. of column,n. of line. (full scale 5000*2500)
  • 2 โ€” goes east observation โ€” enter month,day,decimal hour (universal time-hh.ddd) n. of column,n. of line. (full scale 17000*12000)c
  • 3 โ€” goes west observation โ€” enter month,day,decimal hour (universal time-hh.ddd) n. of column,n. of line. (full scale 17000*12000)
  • 4 โ€” avhrr (PM noaa) โ€” enter month,day,decimal hour (universal time-hh.ddd) n. of column(1-2048),xlonan,hna give long.(xlonan) and overpass hour (hna) at the ascendant node at equator
  • 5 โ€” avhrr (AM noaa) โ€” enter month,day,decimal hour (universal time-hh.ddd) n. of column(1-2048),xlonan,hna give long.(xlonan) and overpass hour (hna) at the ascendant node at equator
  • 6 โ€” hrv (spot) โ€” enter month,day,hh.ddd,long.,lat. *
  • 7 โ€” tm (landsat) โ€” enter month,day,hh.ddd,long.,lat. *
  • 8 โ€” etm+ (landsat7) โ€” enter month,day,hh.ddd,long.,lat. *
  • 9 โ€” liss (IRS 1C) โ€” enter month,day,hh.ddd,long.,lat. *
  • 10 โ€” aster โ€” enter month,day,hh.ddd,long.,lat. *
  • 11 โ€” avnir โ€” enter month,day,hh.ddd,long.,lat. *
  • 12 โ€” ikonos โ€” enter month,day,hh.ddd,long.,lat. *
  • 13 โ€” RapidEye โ€” enter month,day,hh.ddd,long.,lat. *
  • 14 โ€” VGT1 (SPOT4) โ€” enter month,day,hh.ddd,long.,lat. *
  • 15 โ€” VGT2 (SPOT5) โ€” enter month,day,hh.ddd,long.,lat. *
  • 16 โ€” WorldView 2 โ€” enter month,day,hh.ddd,long.,lat. *
  • 17 โ€” QuickBird โ€” enter month,day,hh.ddd,long.,lat. *
  • 18 โ€” LandSat 8 โ€” enter month,day,hh.ddd,long.,lat. *
  • 19 โ€” Geoeye 1 โ€” enter month,day,hh.ddd,long.,lat. *
  • 20 โ€” Spot6 โ€” enter month,day,hh.ddd,long.,lat. *
  • 21 โ€” Spot7 โ€” enter month,day,hh.ddd,long.,lat. *
  • 22 โ€” Pleiades1A โ€” enter month,day,hh.ddd,long.,lat. *
  • 23 โ€” Pleiades1B โ€” enter month,day,hh.ddd,long.,lat. *
  • 24 โ€” Worldview3 โ€” enter month,day,hh.ddd,long.,lat. *
  • 25 โ€” Sentinel-2A โ€” enter month,day,hh.ddd,long.,lat. *
  • 26 โ€” Sentinel-2B โ€” enter month,day,hh.ddd,long.,lat. *
  • 27 โ€” PlanetScope 0c 0d โ€” enter month,day,hh.ddd,long.,lat. *
  • 28 โ€” PlanetScope 0e โ€” enter month,day,hh.ddd,long.,lat. *
  • 29 โ€” PlanetScope 0f 10 โ€” enter month,day,hh.ddd,long.,lat. *
  • 30 โ€” Worldview4 โ€” enter month,day,hh.ddd,long.,lat. *
  • 31 โ€” AVIRIS โ€” enter month,day,hh.ddd,long.,lat. *
  • 32 โ€” Hyperion VNIR โ€” enter month,day,hh.ddd,long.,lat. *
  • 33 โ€” Hyperion SWIR โ€” enter month,day,hh.ddd,long.,lat. *
NOTE: for HRV, TM, ETM+, LISS and ASTER experiments, longitude and latitude are the coordinates of the scene center. Latitude must be > 0 for northern hemisphere and < 0 for southern. Longitude must be > 0 for eastern hemisphere and < 0 for western.

B. Atmospheric model

  • 0 โ€” no gaseous absorption
  • 1 โ€” tropical
  • 2 โ€” midlatitude summer
  • 3 โ€” midlatitude winter
  • 4 โ€” subarctic summer
  • 5 โ€” subarctic winter
  • 6 โ€” us standard 62
  • 7 โ€” okay bestie, define your own atmospheric model as a set of the following 5 parameters per each measurement: altitude [km] pressure [mb] temperature [k] h2o density [g/m3] o3 density [g/m3]. For example: there is one radiosonde measurement for each altitude of 0-25km at a step of 1km, one measurement for each altitude of 25-50km at a step of 5km, and two single measurements for altitudes 70km and 100km. This makes 34 measurements. In that case, there are 34*5 values to input.
  • 8 โ€” define your own atmospheric model providing values of the water vapor and ozone content: uw [g/cm2] uo3 [cm-atm]. The profile is taken from us62.

๐ŸŽ€ C. Aerosols model ๐ŸŽ€

  • 0 โ€” no aerosols
  • 1 โ€” continental model
  • 2 โ€” maritime model
  • 3 โ€” urban model
  • 4 โ€” shettle model for background desert aerosol
  • 5 โ€” biomass burning
  • 6 โ€” stratospheric model
  • 7 โ€” define your own model
    • Enter the volumic percentage of each component: c(1) = volumic % of dust-like c(2) = volumic % of water-soluble c(3) = volumic % of oceanic c(4) = volumic % of soot. All values should be between 0 and 1.
  • 8 โ€” define your own model. Size distribution function: Multimodal Log Normal (up to 4 modes).
  • 9 โ€” define your own model. Size distribution function: Modified gamma.
  • 10 โ€” define your own model. Size distribution function: Junge Power-Law.
  • 11 โ€” define your own model. Sun-photometer measurements, 50 values max, entered as: r and d V / d (logr) where r is the radius [micron], V is the volume, d V / d (logr) [cm3/cm2/micron]. Followed by: nr and ni for each wavelength where nr and ni are respectively the real and imaginary part of the refractive index.

๐ŸŒˆ D. Aerosol concentration model (visibility) ๐ŸŒˆ

If you have an estimate of the meteorological parameter visibility v, enter directly the value of v [km] (the aerosol optical depth (AOD) will be computed from a standard aerosol profile).

If you have an estimate of aerosol optical depth, enter 0 for the visibility and in a following line enter the aerosol optical depth at 550nm (iaer means โ€™iโ€™ for input and โ€™aerโ€™ for aerosol), for example:

0                            - visibility
0.112                        - aerosol optical depth at 550 nm
NOTE: if iaer is 0, enter -1 for visibility.
NOTE: if a visibility map is provided, these parameters are ignored.

๐Ÿฆ‹ E. Target altitude (xps), sensor platform (xpp) ๐Ÿฆ‹

Target altitude (xps, in negative [km]): xps >= 0 means the target is at the sea level. otherwise xps expresses the altitude of the target (e.g., mean elevation) in [km], given as negative value

Sensor platform (xpp, in negative [km] or -1000):

  • xpp = -1000 means that the sensor is on board a satellite.
  • xpp = 0 means that the sensor is at the ground level.
  • -100 < xpp < 0 defines the altitude of the sensor expressed in [km]; this altitude is given relative to the target altitude as negative value.

For aircraft simulations only (xpp is neither equal to 0 nor equal to -1000): puw (water vapor content between the aircraft and the surface) po3 (ozone content between the aircraft and the surface) taerp (the aerosol optical thickness at 550nm between the aircraft and the surface)

If these data are not available, enter negative values for all of them. puw,po3 will then be interpolated from the us62 standard profile according to the values at the ground level; taerp will be computed according to a 2 km exponential profile for aerosol.

๐Ÿ’… F. Sensor band ๐Ÿ’…

There are two possibilities: either define your own spectral conditions (codes -2, -1, 0, or 1) or choose a code indicating the band of one of the pre-defined satellites.

โœจ Define your own spectral conditions โœจ

Note that "wlinf" and "wlsup" refer to the limits of the wavelength range defined by the user for a given simulation. Specifically:

  • wlinf: This represents the lower wavelength limit (or minimum wavelength) of the spectral band for which the simulation is being performed.
  • wlsup: This represents the upper wavelength limit (or maximum wavelength) of the spectral band for the simulation.
  • -2 โ€” Enter wlinf, wlsup. The filter function will be equal to 1 over the whole band (as iwave=0) but step by step output will be printed.
  • -1 โ€” Enter wl (monochr. cond, gaseous absorption is included).
  • 0 โ€” Enter wlinf, wlsup. The filter function will be equal to 1 over the whole band.
  • 1 โ€” Enter wlinf, wlsup and userโ€™s filter function s (lambda) by step of 0.0025 micrometer.

๐Ÿ›ฐ๏ธ Pre-defined satellite bands ๐Ÿ›ฐ๏ธ

  • 2 โ€” meteosat vis band (0.350-1.110)
  • 3 โ€” goes east band vis (0.490-0.900)
  • 4 โ€” goes west band vis (0.490-0.900)
  • 5 โ€” avhrr (noaa6) band 1 (0.550-0.750)
  • 6 โ€” avhrr (noaa6) band 2 (0.690-1.120)
  • 7 โ€” avhrr (noaa7) band 1 (0.500-0.800)
  • 8 โ€” avhrr (noaa7) band 2 (0.640-1.170)
  • 9 โ€” avhrr (noaa8) band 1 (0.540-1.010)
  • 10 โ€” avhrr (noaa8) band 2 (0.680-1.120)
  • 11 โ€” avhrr (noaa9) band 1 (0.530-0.810)
  • 12 โ€” avhrr (noaa9) band 1 (0.680-1.170)
  • 13 โ€” avhrr (noaa10) band 1 (0.530-0.780)
  • 14 โ€” avhrr (noaa10) band 2 (0.600-1.190)
  • 15 โ€” avhrr (noaa11) band 1 (0.540-0.820)
  • 16 โ€” avhrr (noaa11) band 2 (0.600-1.120)
  • 17 โ€” hrv1 (spot1) band 1 (0.470-0.650)
  • 18 โ€” hrv1 (spot1) band 2 (0.600-0.720)
  • 19 โ€” hrv1 (spot1) band 3 (0.730-0.930)
  • 20 โ€” hrv1 (spot1) band pan (0.470-0.790)
  • 21 โ€” hrv2 (spot1) band 1 (0.470-0.650)
  • 22 โ€” hrv2 (spot1) band 2 (0.590-0.730)
  • 23 โ€” hrv2 (spot1) band 3 (0.740-0.940)
  • 24 โ€” hrv2 (spot1) band pan (0.470-0.790)
  • 25 โ€” tm (landsat5) band 1 (0.430-0.560)
  • 26 โ€” tm (landsat5) band 2 (0.500-0.650)
  • 27 โ€” tm (landsat5) band 3 (0.580-0.740)
  • 28 โ€” tm (landsat5) band 4 (0.730-0.950)
  • 29 โ€” tm (landsat5) band 5 (1.5025-1.890)
  • 30 โ€” tm (landsat5) band 7 (1.950-2.410)
  • 31 โ€” mss (landsat5) band 1 (0.475-0.640)
  • 32 โ€” mss (landsat5) band 2 (0.580-0.750)
  • 33 โ€” mss (landsat5) band 3 (0.655-0.855)
  • 34 โ€” mss (landsat5) band 4 (0.785-1.100)
  • 35 โ€” MAS (ER2) band 1 (0.5025-0.5875)
  • 36 โ€” MAS (ER2) band 2 (0.6075-0.7000)

okay so the band list did not come to play ๐Ÿ˜ญ it just keeps going, and we are not skipping a single one of these, so buckle up bestie ๐Ÿ’…

  1. MAS (ER2) band 3 โ€” (0.8300-0.9125) ๐Ÿฆ‹
  2. MAS (ER2) band 4 โ€” (0.9000-0.9975)
  3. MAS (ER2) band 5 โ€” (1.8200-1.9575)
  4. MAS (ER2) band 6 โ€” (2.0950-2.1925)
  5. MAS (ER2) band 7 โ€” (3.5800-3.8700)
  6. MODIS band 1 โ€” (0.6100-0.6850) โœจ
  7. MODIS band 2 โ€” (0.8200-0.9025)
  8. MODIS band 3 โ€” (0.4500-0.4825)
  9. MODIS band 4 โ€” (0.5400-0.5700)
  10. MODIS band 5 โ€” (1.2150-1.2700)
  11. MODIS band 6 โ€” (1.6000-1.6650)
  12. MODIS band 7 โ€” (2.0575-2.1825)
  13. avhrr (noaa12) band 1 โ€” (0.500-1.000) ๐ŸŒธ
  14. avhrr (noaa12) band 2 โ€” (0.650-1.120)
  15. avhrr (noaa14) band 1 โ€” (0.500-1.110)
  16. avhrr (noaa14) band 2 โ€” (0.680-1.100)

POLDER really said "we come with receipts" โ€” the little (non polar) notes and the P1 tags are part of the whole vibe, so take them as they come ๐Ÿ’œ

  1. POLDER band 1 โ€” (0.4125-0.4775) ๐ŸŒˆ
  2. POLDER band 2 (non polar) โ€” (0.4100-0.5225)
  3. POLDER band 3 (non polar) โ€” (0.5325-0.5950)
  4. POLDER band 4 P1 โ€” (0.6300-0.7025)
  5. POLDER band 5 (non polar) โ€” (0.7450-0.7800)
  6. POLDER band 6 (non polar) โ€” (0.7000-0.8300)
  7. POLDER band 7 P1 โ€” (0.8100-0.9200)
  8. POLDER band 8 (non polar) โ€” (0.8650-0.9400)
  9. etm+ (landsat7) band 1 blue โ€” (435nm - 517nm) ๐Ÿ’™
  10. etm+ (landsat7) band 2 green โ€” (508nm - 617nm)
  11. etm+ (landsat7) band 3 red โ€” (625nm - 702nm)
  12. etm+ (landsat7) band 4 NIR โ€” (753nm - 910nm)
  13. etm+ (landsat7) band 5 SWIR โ€” (1520nm - 1785nm)
  14. etm+ (landsat7) band 7 SWIR โ€” (2028nm - 2375nm)
  15. etm+ (landsat7) band 8 PAN โ€” (505nm - 917nm)
  16. liss (IRC 1C) band 2 โ€” (0.502-0.620) ๐ŸŽ€
  17. liss (IRC 1C) band 3 โ€” (0.612-0.700)
  18. liss (IRC 1C) band 4 โ€” (0.752-0.880)
  19. liss (IRC 1C) band 5 โ€” (1.452-1.760)

aster showed up with NINE whole bands like it's got nothing to prove ๐Ÿ‘‘ iconic honestly

  1. aster band 1 โ€” (0.480-0.645) โญ
  2. aster band 2 โ€” (0.588-0.733)
  3. aster band 3N โ€” (0.723-0.913)
  4. aster band 4 โ€” (1.530-1.750)
  5. aster band 5 โ€” (2.103-2.285)
  6. aster band 6 โ€” (2.105-2.298)
  7. aster band 7 โ€” (2.200-2.393)
  8. aster band 8 โ€” (2.248-2.475)
  9. aster band 9 โ€” (2.295-2.538)
  10. avnir band 1 โ€” (408nm - 517nm) ๐Ÿซถ
  11. avnir band 2 โ€” (503nm - 612nm)
  12. avnir band 3 โ€” (583nm - 717nm)
  13. avnir band 4 โ€” (735nm - 922nm)
  14. Ikonos Green band โ€” (408nm - 642nm) ๐Ÿ’š
  15. Ikonos Red band โ€” (448nm - 715nm)
  16. Ikonos NIR band โ€” (575nm - 787nm)
  17. RapidEye Blue band โ€” (440nm - 512nm) ๐ŸŒŠ
  18. RapidEye Green band โ€” (515nm - 592nm)
  19. RapidEye Red band โ€” (628nm - 687nm)
  20. RapidEye Red edge band โ€” (685nm - 735nm)
  21. RapidEye NIR band โ€” (750nm - 860nm)
  22. VGT1 (SPOT4) band 0 โ€” (420nm - 497nm) ๐Ÿฆ‹
  23. VGT1 (SPOT4) band 2 โ€” (603nm - 747nm)
  24. VGT1 (SPOT4) band 3 โ€” (740nm - 942nm)
  25. VGT1 (SPOT4) MIR band โ€” (1540nm - 1777nm)
  26. VGT2 (SPOT5) band 0 โ€” (423nm - 492nm) ๐Ÿฆ‹
  27. VGT2 (SPOT5) band 2 โ€” (600nm - 737nm)
  28. VGT2 (SPOT5) band 3 โ€” (745nm - 945nm)
  29. VGT2 (SPOT5) MIR band โ€” (1523nm - 1757nm)

WorldView2 walking in with the Panchromatic + Coastal Blue + Yellow + Red edge + NIR1 + NIR2 combo, like, ok main character energy fr ๐Ÿ’…

  1. WorldView2 Panchromatic band โ€” (448nm - 812nm) ๐ŸŒŸ
  2. WorldView2 Coastal Blue band โ€” (395nm - 457nm)
  3. WorldView2 Blue band โ€” (440nm - 517nm)
  4. WorldView2 Green band โ€” (503nm - 587nm)
  5. WorldView2 Yellow band โ€” (583nm - 632nm)
  6. WorldView2 Red band โ€” (623nm - 695nm)
  7. WorldView2 Red edge band โ€” (698nm - 750nm)
  8. WorldView2 NIR1 band โ€” (760nm - 905nm)
  9. WorldView2 NIR2 band โ€” (853nm - 1047nm)
  10. QuickBird Panchromatic band โ€” (385nm - 1060nm) ๐Ÿฆ
  11. QuickBird Blue band โ€” (420nm - 585nm)
  12. QuickBird Green band โ€” (448nm - 682nm)
  13. QuickBird Red band โ€” (560nm - 747nm)
  14. QuickBird NIR1 band โ€” (650nm - 935nm)

Landsat 8 said "let's do the entire rainbow and then some" ๐ŸŒˆ and honestly? we love that for them

  1. Landsat 8 Coastal aerosol band โ€” (433nm - 455nm) ๐Ÿ’–
  2. Landsat 8 Blue band โ€” (448nm - 515nm)
  3. Landsat 8 Green band โ€” (525nm - 595nm)
  4. Landsat 8 Red band โ€” (633nm - 677nm)
  5. Landsat 8 Panchromatic band โ€” (498nm - 682nm)
  6. Landsat 8 NIR band โ€” (845nm - 885nm)
  7. Landsat 8 Cirrus band โ€” (1355nm - 1390nm)
  8. Landsat 8 SWIR1 band โ€” (1540nm - 1672nm)
  9. Landsat 8 SWIR2 band โ€” (2073nm - 2322nm)
  10. GeoEye 1 Panchromatic band โ€” (448nm - 812nm) ๐Ÿ‘๏ธ
  11. GeoEye 1 Blue band โ€” (443nm - 525nm)
  12. GeoEye 1 Green band โ€” (503nm - 587nm)
  13. GeoEye 1 Red band โ€” (653nm - 697nm)
  14. GeoEye 1 NIR band โ€” (770nm - 932nm)
  15. Spot6 Blue band โ€” (440nm - 532nm) ๐Ÿ›ฐ๏ธ
  16. Spot6 Green band โ€” (515nm - 600nm)
  17. Spot6 Red band โ€” (610nm - 710nm)
  18. Spot6 NIR band โ€” (738nm - 897nm)
  19. Spot6 Pan band โ€” (438nm - 760nm)
  20. Spot7 Blue band โ€” (445nm - 532nm) ๐Ÿ›ฐ๏ธ
  21. Spot7 Green band โ€” (525nm - 607nm)
  22. Spot7 Red band โ€” (610nm - 727nm)
  23. Spot7 NIR band โ€” (745nm - 902nm)
  24. Spot7 Pan band โ€” (443nm - 760nm)

Pleiades1A and Pleiades1B are out here twinning, matching bands, matching energy ๐Ÿซถ

  1. Pleiades1A Blue band โ€” (433nm - 560nm) ๐Ÿ’™
  2. Pleiades1A Green band โ€” (500nm - 617nm)
  3. Pleiades1A Red band โ€” (590nm - 722nm)
  4. Pleiades1A NIR band โ€” (740nm - 945nm)
  5. Pleiades1A Pan band โ€” (460nm - 845nm)
  6. Pleiades1B Blue band โ€” 438nm - 560nm) ๐Ÿ™ˆ
ok don't come for me, that missing opening bracket is upstream's, not mine โ€” i'm just reporting it faithfully, no cap. leaving it exactly as it lives.
  1. Pleiades1B Green band โ€” (498nm - 615nm)
  2. Pleiades1B Red band โ€” (608nm - 727nm)
  3. Pleiades1B NIR band โ€” (750nm - 945nm)
  4. Pleiades1B Pan band โ€” (460nm - 845nm)
  5. Worldview3 Pan band โ€” (445nm - 812nm) ๐Ÿ”ฅ
  6. Worldview3 Coastal blue band โ€” (395nm - 455nm)

and bestieโ€ฆ we are still not done ๐Ÿ˜ญ hold on, there's more coming ๐ŸŒธ

  • 151 โ€” Worldview3 Blue band (443nm - 517nm)
  • 152 โ€” Worldview3 Green band (508nm - 587nm)
  • 153 โ€” Worldview3 Yellow band (580nm - 630nm)
  • 154 โ€” Worldview3 Red band (625nm - 697nm)
  • 155 โ€” Worldview3 Red edge band (698nm - 752nm)
  • 156 โ€” Worldview3 NIR1 band (760nm - 902nm)
  • 157 โ€” Worldview3 NIR2 band (855nm - 1042nm)
  • 158 โ€” Worldview3 SWIR1 band (1178nm - 1242nm)
  • 159 โ€” Worldview3 SWIR2 band (1545nm - 1600nm)
  • 160 โ€” Worldview3 SWIR3 band (1633nm - 1687nm)
  • 161 โ€” Worldview3 SWIR4 band (1698nm - 1762nm)
  • 162 โ€” Worldview3 SWIR5 band (2133nm - 2195nm)
  • 163 โ€” Worldview3 SWIR6 band (2170nm - 2235nm)
  • 164 โ€” Worldview3 SWIR7 band (2225nm - 2295nm)
  • 165 โ€” Worldview3 SWIR8 band (2283nm - 2377nm)
  • 166 โ€” Sentinel2A Coastal blue band B1 (430nm - 455nm)
  • 167 โ€” Sentinel2A Blue band B2 (440nm - 530nm)
  • 168 โ€” Sentinel2A Green band B3 (540nm - 580nm)
  • 169 โ€” Sentinel2A Red band B4 (648nm - 682nm)
  • 170 โ€” Sentinel2A Red edge band B5 (695nm - 712nm)
  • 171 โ€” Sentinel2A Red edge band B6 (733nm - 747nm)
  • 172 โ€” Sentinel2A Red edge band B7 (770nm - 795nm)
  • 173 โ€” Sentinel2A NIR band B8 (775nm - 905nm)
  • 174 โ€” Sentinel2A Red edge band B8A (850nm - 880nm)
  • 175 โ€” Sentinel2A Water vapour band B9 (933nm - 957nm)
  • 176 โ€” Sentinel2A SWIR Cirrus band B10 (1355nm - 1392nm)
  • 177 โ€” Sentinel2A SWIR band B11 (1558nm - 1667nm)
  • 178 โ€” Sentinel2A SWIR band B12 (2088nm - 2315nm)
  • 179 โ€” Sentinel2B Coastal blue band B1 (430nm - 455nm)
  • 180 โ€” Sentinel2B Blue band B2 (440nm - 530nm)
  • 181 โ€” Sentinel2B Green band B3 (538nm - 580nm)
  • 182 โ€” Sentinel2B Red band B4 (648nm - 682nm)
  • 183 โ€” Sentinel2B Red edge band B5 (695nm - 712nm)
  • 184 โ€” Sentinel2B Red edge band B6 (730nm - 747nm)
  • 185 โ€” Sentinel2B Red edge band B7 (768nm - 792nm)
  • 186 โ€” Sentinel2B NIR band B8 (778nm - 905nm)
  • 187 โ€” Sentinel2B Red edge band B8A (850nm - 877nm)
  • 188 โ€” Sentinel2B Water vapour band B9 (930nm - 955nm)
  • 189 โ€” Sentinel2B SWIR Cirrus band B10 (1358nm - 1397nm)
  • 190 โ€” Sentinel2B SWIR band B11 (1555nm - 1667nm)
  • 191 โ€” Sentinel2B SWIR band B12 (2075nm - 2300nm)
  • 192 โ€” PlanetScope 0c 0d Blue band B1 (440nm - 570nm)
  • 193 โ€” PlanetScope 0c 0d Green band B2 (450nm - 690nm)
  • 194 โ€” PlanetScope 0c 0d Red band B3 (460nm - 700nm)
  • 195 โ€” PlanetScope 0c 0d NIR band B4 (770nm - 880nm)
  • 196 โ€” PlanetScope 0e Blue band B1 (430nm - 700nm)
  • 197 โ€” PlanetScope 0e Green band B2 (450nm - 700nm)
  • 198 โ€” PlanetScope 0e Red band B3 (460nm - 700nm)
  • 199 โ€” PlanetScope 0e NIR band B4 (760nm - 880nm)
  • 200 โ€” PlanetScope 0f 10 Blue band B1 (450nm - 680nm)
  • 201 โ€” PlanetScope 0f 10 Green band B2 (450nm - 680nm)
  • 202 โ€” PlanetScope 0f 10 Red band B3 (450nm - 680nm)
  • 203 โ€” PlanetScope 0f 10 NIR band B4 (760nm - 870nm)
  • 204 โ€” Worldview4 Pan band (424nm - 842nm)
  • 205 โ€” Worldview4 Blue band (416nm - 567nm)
  • 206 โ€” Worldview4 Green band (488nm - 626nm)
  • 207 โ€” Worldview4 Red band (639nm - 711nm)
  • 208 โ€” Worldview4 NIR1 band (732nm - 962nm)
  • 209 โ€” AVIRIS b1 band (365nm)
  • 210 โ€” AVIRIS b2 band (375nm)
  • . โ€” AVIRIS b. band (+10nm)
  • 431 โ€” AVIRIS b223 band (2486nm)
  • 432 โ€” AVIRIS b224 band (2496nm)
  • 433 โ€” Hyperion VNIR b8 band (427nm)
  • 434 โ€” Hyperion VNIR b9 band (437.16326nm)
  • . โ€” Hyperion VNIR b. band (+10.16326nm)
  • 480 โ€” Hyperion VNIR b56 band (914.83648nm)
  • 481 โ€” Hyperion VNIR b57 band (924.99974nm)
  • 482 โ€” Hyperion SWIR b77 band (912nm)
  • 483 โ€” Hyperion SWIR b78 band (922.0884nm)
  • . โ€” Hyperion SWIR b. band (+10.0884nm)
  • 627 โ€” Hyperion SWIR b223 band (2384.9064nm)
  • 628 โ€” Hyperion SWIR b224 band (2394.9948nm)

๐ŸŒˆ EXAMPLES ๐ŸŒˆ

โœจ Atmospheric correction of a Sentinel-2 band โœจ

okay bestie so this example is literally about doing atmospheric correction on a Sentinel-2 scene in the North Carolina project ๐Ÿซถ

So picture this: you've got a Sentinel-2 L1C scene named

S2A_OPER_PRD_MSIL1C_PDMC_20161029T092602_R054_V20161028T155402_20161028T155402

and you downloaded it and imported it with region cropping (peep r.import for that part ๐Ÿ‘€) into the PERMANENT mapset of the North Carolina project. The computational region was set to the extent of the elevation map in the North Carolina dataset. Now you're sitting there with 13 individual bands (B01-B12) that you want the atmospheric correction applied to. The following steps get applied to each band separately, one at a time, no skipping ๐Ÿ’…

๐ŸŽ€ Create the parameters file for i.atcorr ๐ŸŽ€

Step one, bestie: we make a file that holds the 6S parameters for one specific scene and one specific band. To cook up a 6S file, we need to gather up this info:

  • geometrical conditions,
  • moth, day, decimal hours in GMT, decimal longitude and latitude of measurement,
  • atmospheric model,
  • aerosol model,
  • visibility or aerosol optical depth,
  • mean target elevation above sea level,
  • sensor height and,
  • sensor band.
yes bestie, the original really does say "moth" ๐Ÿฆ‹ โ€” we're quoting the vibe faithfully, no cap.

1 โ€” Geometrical conditions

For Sentinel-2A, the geometrical conditions take the value 25, and for Sentinel-2B the geometrical conditions value is 26 (see table A). Our scene comes from the Sentinel-2A mission โ€” you can tell because the file name starts with S2A_... โœจ

2 โ€” Day, time, longitude and latitude of measurement

The day and the time of the measurement are lowkey hiding in the filename โ€” specifically the second datum in the file name, with format YYYYMMDDTHHMMSS โ€” and they're also noted in the metadata file, which is included in the downloaded scene (the file with the .xml extension).

Our sample scene was taken on October 28th (20161028) at 15:54:02 (155402). Real talk though: the time has to be specified in decimal hours in Greenwich Mean Time (GMT). Good news, the time in the scene name is already in GMT, so we just convert it like this:

15 + 54/60 + 2/3600 = 15.901

Longitude and latitude refer to the centre of the computational region (which can totally be smaller than the scene), and they must be in WGS84 decimal coordinates. To grab the centre coords, run:

g.region -bg

The longitude and latitude of the centre are stored in ll_clon and ll_clat. In our case: ll_clon=-78.691 and ll_clat=35.749 ๐Ÿ—บ๏ธ

3 โ€” Atmospheric model

You get to pick between the various atmospheric models defined at the beginning of this manual. For North Carolina, we can choose 2 - midlatitude summer ๐ŸŒž

4 โ€” Aerosol model

Same deal โ€” there are various aerosol models as defined at the beginning of this manual, pick your fave. For North Carolina, we can choose 1 - continental model ๐ŸŒซ๏ธ

5 โ€” Visibility or Aerosol Optical Depth

For Sentinel-2 scenes, the visibility is not measured, so instead we have to estimate the aerosol optical depth, e.g. from AERONET. With a bit of luck ๐Ÿ“ you can find a station nearby your location that measured the Aerosol Optical Depth at 500 nm at the same time as the scene was taken. In our case, on 28th October 2016, the EPA-Res_Triangle_Pk station measured AOD = 0.07 (approximately).

6 โ€” Mean target elevation above sea level

Mean target elevation above sea level = the mean elevation of the computational region. You can estimate it from the digital elevation model, e.g. by running:

r.univar -g elevation

The mean elevation is stored in mean. In our case, mean=110. In the 6S file it will be displayed in [-km], i.e., -0.110 โ›ฐ๏ธ

  1. Sensor height ๐Ÿ›ฐ๏ธ โ€” bestie, our sensor is literally up on a satellite, so you set this one to -1000. Every single time. No thoughts, just -1000.
  2. Sensor band ๐ŸŒˆ โ€” the whole glow-up of satellite bands lives in table F (scroll up, it's right there โ˜๏ธ). For Sentinel-2A the band numbers go from 166 to 178, and for Sentinel-2B they run from 179 to 191.

Okay so this is what the 6S file looks like for Band 02 of our scene ๐Ÿ‘‡ and to actually use it in i.atcorr we just save it as a text file, like params_B02.txt for example. Slay.

25
10 28 15.901 -78.691 35.749
2
1
0
0.07
-0.110
-1000
167

โœจ Compute atmospheric correction โœจ

Next up, we run i.atcorr on the band we picked, B02 of our Sentinel 2 scene ๐Ÿ’–. Parameters we gotta feed it:

  • input โ€” the raster band to be processed, duh.
  • parameters โ€” path to the 6S file we just created in the previous step (tho ngl you could also just enter the values directly).
  • output โ€” the name for your output corrected raster band ๐ŸŽ€
  • range โ€” from 1 to the QUANTIFICATION_VALUE stored in the metadata file. For both Sentinel-2A and Sentinel-2B that value is 10000.
  • rescale โ€” the output range of values for the corrected bands. This one is up to you, main character's choice ๐Ÿ’… for example: 0-255, 0-1, 1-10000.

And if the data is available, you can also specify these ๐Ÿ‘‘:

  • elevation โ€” raster of digital elevation model
  • visibility โ€” raster of visibility model

Finally, this is the command to apply atmospheric correction to band B02:

i.atcorr input=B02 parameters=params_B02.txt output=B02.atcorr range=1,10000 rescale=0,255 elevation=elevation

And here's the tea ๐Ÿ“: to apply atmospheric correction to the remaining bands, only the last line in the 6S parameters file (that's the sensor band, bestie) needs changing. Everything else stays exactly the same. Lowkey iconic.

Figure: Sentinel-2A Band 02 with applied atmospheric correction (histogram equalization grayscale color scheme)

๐Ÿ›ฐ๏ธ Atmospheric correction of a Landsat-7 band ๐Ÿ›ฐ๏ธ

This example is also based on the North Carolina sample dataset (GMT -5 hours). First we set the computational region to the satellite map, like band 4:

g.region raster=lsat7_2002_40 -p

It is so important to verify the available metadata for the sun position, because it has to be defined for the atmospheric correction. One option: check the satellite overpass time with the sun position as reported in the metadata file (file copy; North Carolina sample dataset). For the North Carolina sample dataset, these values have been stored for each channel and you can grab them with:

r.info lsat7_2002_40

In this case we have: SUN_AZIMUTH = 120.8810347, SUN_ELEVATION = 64.7730999.

If the sun position metadata are unavailable ๐Ÿ™ˆ, we can also calculate them from the overpass time like this (r.sunmask uses SOLPOS):

r.sunmask -s elev=elevation out=dummy year=2002 month=5 day=24 hour=10 min=42 sec=7 timezone=-5
# .. reports: sun azimuth: 121.342461, sun angle above horz.(refraction corrected): 65.396652

And if the overpass time is unknown, use the NASA LaRC Satellite Overpass Predictor. Periodt.

๐Ÿงฎ Convert digital numbers (DN) to radiance at top-of-atmosphere (TOA) ๐Ÿงฎ

For Landsat and ASTER, the conversion can be conveniently done with i.landsat.toar or i.aster.toar, respectively. Chef's kiss.

In case of different satellites, the conversion of DN (digital number = pixel values) to radiance at top-of-atmosphere (TOA) can also be done manually, using e.g. the formula:

# formula depends on satellite sensor, see respective metadata
Lฮป = ((LMAXฮป - LMINฮป)/(QCALMAX-QCALMIN)) * (QCAL-QCALMIN) + LMINฮป

where,

  • Lฮป = Spectral Radiance at the sensor's aperture in Watt/(meter squared * ster * ยตm), the apparent radiance as seen by the satellite sensor;
  • QCAL = the quantized calibrated pixel value in DN;
  • LMINฮป = the spectral radiance that is scaled to QCALMIN in watts/(meter squared * ster * ยตm);
  • LMAXฮป = the spectral radiance that is scaled to QCALMAX in watts/(meter squared * ster * ยตm);
  • QCALMIN = the minimum quantized calibrated pixel value (corresponding to LMINฮป) in DN;
  • QCALMAX = the maximum quantized calibrated pixel value (corresponding to LMAXฮป) in DN=255.

LMINฮป and LMAXฮป are the radiances related to the minimal and maximal DN value, and they are reported in the metadata file of each image. High gain or low gain is also reported in the metadata file of each satellite image. For Landsat ETM+, the minimal DN value (QCALMIN) is 1 (see Landsat handbook, chapter 11), and the maximal DN value (QCALMAX) is 255. QCAL is the DN value for every separate pixel in the Landsat image.

We extract the coefficients and apply them in order to obtain the radiance map ๐Ÿ’œ:

CHAN=4
r.info lsat7_2002_${CHAN}0 -h | tr โ€™\nโ€™ โ€™ โ€™ | sed โ€™s+ ++gโ€™ | tr โ€™:โ€™ โ€™\nโ€™ | grep "LMIN_BAND${CHAN}\|LMAX_BAND${CHAN}"
LMAX_BAND4=241.100,p016r035_7x20020524.met
LMIN_BAND4=-5.100,p016r035_7x20020524.met
QCALMAX_BAND4=255.0,p016r035_7x20020524.met
QCALMIN_BAND4=1.0,p016r035_7x20020524.met

Conversion to radiance (this calculation is done for band 4, for the other bands, the numbers will need to be replaced with their related values):

r.mapcalc "lsat7_2002_40_rad = ((241.1 - (-5.1)) / (255.0 - 1.0)) * (lsat7_2002_40 - 1.0) + (-5.1)"

Again, the r.mapcalc calculation is only needed when working with satellite data other than Landsat or ASTER ๐Ÿซถ.

๐Ÿ“ Create the parameters file for i.atcorr ๐Ÿ“

The underlying 6S model is parametrized through a control file, indicated with the parameters option. This is a text file defining geometrical and atmospherical conditions of the satellite overpass. Here we create a control file icnd_lsat4.txt for band 4 (NIR), based on metadata. For the overpass time, we need to define decimal hours:

10:42:07 NC local time = 10.70 decimal hours (decimal minutes: 42 * 100 / 60) which is 15.70 GMT.

8                            - geometrical conditions=Landsat ETM+
5 24 15.70 -78.691 35.749    - month day hh.ddd longitude latitude ("hh.ddd" is in GMT decimal hours)
2                            - atmospheric model=midlatitude summer
1                            - aerosols model=continental
50                           - visibility [km] (aerosol model concentration)
-0.110                       - mean target elevation above sea level [km]
-1000                        - sensor on board a satellite
64                           - 4th band of ETM+ Landsat 7

Finally, run the atmospheric correction (-r for reflectance input map; -a for date > July 2000):

i.atcorr -r -a lsat7_2002_40_rad elevation=elevation parameters=icnd_lsat4.txt output=lsat7_2002_40_atcorr
Okay important bestie ๐Ÿ’€ โ€” note that the altitude value from 'icnd_lsat4.txt' file is read at the beginning to compute the initial transform. Therefore, it is necessary to provide a value that might be the mean value of the elevation model (r.univar elevation). For the atmospheric correction per se, the elevation values from the raster map are used.
Also note the process is computationally intensive ๐Ÿ˜ญ. And note that i.atcorr reports solar elevation angle above horizon rather than solar zenith angle. That's the plot twist, don't @ me.

๐Ÿšง REMAINING DOCUMENTATION ISSUES ๐Ÿšง

The influence and importance of the visibility value or map should be explained, also how to obtain an estimate for either visibility or aerosol optical depth at 550nm.

๐Ÿ“š REFERENCES ๐Ÿ“š

  • Vermote, E.F., Tanre, D., Deuze, J.L., Herman, M., and Morcrette, J.J., 1997, Second simulation of the satellite signal in the solar spectrum, 6S: An overview., IEEE Trans. Geosc. and Remote Sens. 35(3):675-686.
  • 6S Manual: PDF1, PDF2, and PDF3
  • RapidEye sensors have been provided by RapidEye AG, Germany
  • Barsi, J.A., Markham, B.L. and Pedelty, J.A., 2011, The operational land imager: spectral response and spectral uniformity., Proc. SPIE 8153, 81530G; doi:10.1117/12.895438

๐Ÿ‘€ SEE ALSO ๐Ÿ‘€

  • GRASS Wiki page about Atmospheric correction
  • i.aster.toar
  • i.colors.enhance
  • i.landsat.toar
  • r.info
  • r.mapcalc
  • r.univar

๐Ÿ’– AUTHORS ๐Ÿ’–

Original version of the program for GRASS 5:

Christo Zietsman, 13422863(at)sun.ac.za

Code clean-up and port to GRASS 6.3, 15.12.2006:

Yann Chemin, ychemin(at)gmail.com

Documentation clean-up + IRS LISS sensor addition 5/2009:

Markus Neteler, FEM, Italy

ASTER sensor addition 7/2009:

Michael Perdue, Canada

AVNIR, IKONOS sensors addition 7/2010:

Daniel Victoria, Anne Ghisla

RapidEye sensors addition 11/2010:

Peter Lรถwe, Anne Ghisla

VGT1 and VGT2 sensors addition from 6SV-1.1 sources, addition 07/2011:

Alfredo Alessandrini, Anne Ghisla

Added Landsat 8 from NASA sources, addition 05/2014:

Nikolaos Ves

Geoeye1 addition 7/2015:

Marco Vizzari

Worldview3 addition 8/2016:

Markus Neteler, mundialis.de, Germany

Sentinel-2A addition 12/2016:

Markus Neteler, mundialis.de, Germany

Sentinel-2B addition 1/2018:

Stefan Blumentrath, Zofie Cimburova, Norwegian Institute for Nature Research, NINA, Oslo, Norway

Worldview4 addition 12/2018:

Markus Neteler, mundialis.de, Germany

AVIRIS/Hyperion addition 11/2023:

Yann Chemin, SOPHIA Engineering, FR

๐ŸŒŸ SOURCE CODE ๐ŸŒŸ

Available at: i.atcorr source code (history)

Latest change: Friday Apr 17 01:04:26 2026 in commit: 32a143358df26b2e518b03c7e0d79541e8d1e5b1

Main index | Imagery index | Topics index | Keywords index | Graphical index | Full index

ยฉ 2003-2026 GRASS Development Team, GRASS 8.5.0 Reference Manual

and that's the whole page bestie, we ate and left absolutely no crumbs ๐Ÿ’… this lil love letter to atmospheric correction comes straight outta the GRASS 8.5.0 Reference Manual for i.atcorr (1grass), with its latest change dated Friday Apr 17 01:04:26 2026 ๐Ÿฆ‹ go correct some atmospheres, you absolute icon ๐Ÿ‘‘โœจ

๐Ÿ“ฆ where this page comes from