GRASS GIS manual: i.atcorr

## NAME

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

## KEYWORDS

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

## SYNOPSIS

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

### Parameters:

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

## DESCRIPTION

i.atcorr performs atmospheric correction on the 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.

Important note: 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. This flag tells i.atcorr to try and speedup calculations. However, this option will increase memory requirements.

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

Note that the satellite overpass time has to be specified in Greenwich Mean Time (GMT).

An example 6S parameters:

    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 mode=tropical
    1                            - aerosols model=continental
    15                           - visibility [km] (aerosol model concentration)
    -0.600                       - mean target elevation above sea level [km] (here 600m 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 projection.

## 6S CODE PARAMETER CHOICES

### A. Geometrical conditions

Code | Description | Details
1 | meteosat observation | enter month,day,decimal hour (universal time-hh.ddd) n. of column,n. of line. (full scale 5000*2500)
2 | goes east observation | enter month,day,decimal hour (universal time-hh.ddd) n. of column,n. of line. (full scale 17000*12000)c
3 | goes west observation | enter month,day,decimal hour (universal time-hh.ddd) n. of column,n. of line. (full scale 17000*12000)
4 | avhrr (PM noaa) | 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 | avhrr (AM noaa) | 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 | hrv (spot) | enter month,day,hh.ddd,long.,lat. *
7 | tm (landsat) | enter month,day,hh.ddd,long.,lat. *
8 | etm+ (landsat7) | enter month,day,hh.ddd,long.,lat. *
9 | liss (IRS 1C) | enter month,day,hh.ddd,long.,lat. *
10 | aster | enter month,day,hh.ddd,long.,lat. *
11 | avnir | enter month,day,hh.ddd,long.,lat. *
12 | ikonos | enter month,day,hh.ddd,long.,lat. *
13 | RapidEye | enter month,day,hh.ddd,long.,lat. *
14 | VGT1 (SPOT4) | enter month,day,hh.ddd,long.,lat. *
15 | VGT2 (SPOT5) | enter month,day,hh.ddd,long.,lat. *
16 | WorldView 2 | enter month,day,hh.ddd,long.,lat. *
17 | QuickBird | enter month,day,hh.ddd,long.,lat. *
18 | LandSat 8 | enter month,day,hh.ddd,long.,lat. *
19 | Geoeye 1 | 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

Code | Meaning
0 | no gaseous absorption
1 | tropical
2 | midlatitude summer
3 | midlatitude winter
4 | subarctic summer
5 | subarctic winter
6 | us standard 62
7 | 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 measurment for each altitude of 25-50km at a step of 5km, and two single measurements for altitudes 70km and 100km. This makes 34 measurments. In that case, there are 34*5 values to input.
8 | 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.

### C. Aerosols model

Code | Meaning | Details
0 | no aerosols |
1 | continental model |
2 | maritime model |
3 | urban model |
4 | shettle model for background desert aerosol |
5 | biomass burning |
6 | stratospheric model |
7 | 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 between 0 and 1.
8 | define your own model | Size distribution function: Multimodal Log Normal (up to 4 modes).
9 | define your own model | Size distribution function: Modified gamma.
10 | define your own model | Size distribution function: Junge Power-Law.
11 | 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.

### D. Aerosol concentration model (visibility)

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 'i' for input and 'aer' for aerosol), for example:

    0                            - visibility
    0.112                        - aerosol optical depth 550 nm

NOTE: if iaer is 0, enter -1 for visibility.

### E. Target altitude (xps), sensor platform (xpp)

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,po3 (water vapor content,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 2km exponential profile for aerosol.

### F. Sensor band

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.

Define your own spectral conditions:

Code | Meaning
-2 | 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 | Enter wl (monochr. cond, gaseous absorption is included).
0 | Enter wlinf, wlsup. The filter function will be equal to 1over the whole band.
1 | Enter wlinf, wlsup and user's filter function s(lambda) by step of 0.0025 micrometer.

Pre-defined satellite bands:

Code | Meaning
2 | meteosat vis band (0.350-1.110)
3 | goes east band vis (0.490-0.900)
4 | goes west band vis (0.490-0.900)
5 | avhrr (noaa6) band 1 (0.550-0.750)
6 | avhrr (noaa6) band 2 (0.690-1.120)
7 | avhrr (noaa7) band 1 (0.500-0.800)
8 | avhrr (noaa7) band 2 (0.640-1.170)
9 | avhrr (noaa8) band 1 (0.540-1.010)
10 | avhrr (noaa8) band 2 (0.680-1.120)
11 | avhrr (noaa9) band 1 (0.530-0.810)
12 | avhrr (noaa9) band 1 (0.680-1.170)
13 | avhrr (noaa10) band 1 (0.530-0.780)
14 | avhrr (noaa10) band 2 (0.600-1.190)
15 | avhrr (noaa11) band 1 (0.540-0.820)
16 | avhrr (noaa11) band 2 (0.600-1.120)
17 | hrv1 (spot1) band 1 (0.470-0.650)
18 | hrv1 (spot1) band 2 (0.600-0.720)
19 | hrv1 (spot1) band 3 (0.730-0.930)
20 | hrv1 (spot1) band pan (0.470-0.790)
21 | hrv2 (spot1) band 1 (0.470-0.650)
22 | hrv2 (spot1) band 2 (0.590-0.730)
23 | hrv2 (spot1) band 3 (0.740-0.940)
24 | hrv2 (spot1) band pan (0.470-0.790)
25 | tm (landsat5) band 1 (0.430-0.560)
26 | tm (landsat5) band 2 (0.500-0.650)
27 | tm (landsat5) band 3 (0.580-0.740)
28 | tm (landsat5) band 4 (0.730-0.950)
29 | tm (landsat5) band 5 (1.5025-1.890)
30 | tm (landsat5) band 7 (1.950-2.410)
31 | mss (landsat5) band 1 (0.475-0.640)
32 | mss (landsat5) band 2 (0.580-0.750)
33 | mss (landsat5) band 3 (0.655-0.855)
34 | mss (landsat5) band 4 (0.785-1.100)
35 | MAS (ER2) band 1 (0.5025-0.5875)
36 | MAS (ER2) band 2 (0.6075-0.7000)
37 | MAS (ER2) band 3 (0.8300-0.9125)
38 | MAS (ER2) band 4 (0.9000-0.9975)
39 | MAS (ER2) band 5 (1.8200-1.9575)
40 | MAS (ER2) band 6 (2.0950-2.1925)
41 | MAS (ER2) band 7 (3.5800-3.8700)
42 | MODIS band 1 (0.6100-0.6850)
43 | MODIS band 2 (0.8200-0.9025)
44 | MODIS band 3 (0.4500-0.4825)
45 | MODIS band 4 (0.5400-0.5700)
46 | MODIS band 5 (1.2150-1.2700)
47 | MODIS band 6 (1.6000-1.6650)
48 | MODIS band 7 (2.0575-2.1825)
49 | avhrr (noaa12) band 1 (0.500-1.000)
50 | avhrr (noaa12) band 2 (0.650-1.120)
51 | avhrr (noaa14) band 1 (0.500-1.110)
52 | avhrr (noaa14) band 2 (0.680-1.100)
53 | POLDER band 1 (0.4125-0.4775)
54 | POLDER band 2 (non polar) (0.4100-0.5225)
55 | POLDER band 3 (non polar) (0.5325-0.5950)
56 | POLDER band 4 P1 (0.6300-0.7025)
57 | POLDER band 5 (non polar) (0.7450-0.7800)
58 | POLDER band 6 (non polar) (0.7000-0.8300)
59 | POLDER band 7 P1 (0.8100-0.9200)
60 | POLDER band 8 (non polar) (0.8650-0.9400)
61 | etm+ (landsat7) band 1 (0.435-0.520)
62 | etm+ (landsat7) band 2 (0.506-0.621)
63 | etm+ (landsat7) band 3 (0.622-0.702)
64 | etm+ (landsat7) band 4 (0.751-0.911)
65 | etm+ (landsat7) band 5 (1.512-1.792)
66 | etm+ (landsat7) band 7 (2.020-2.380)
67 | etm+ (landsat7) band 8 (0.504-0.909)
68 | liss (IRC 1C) band 2 (0.502-0.620)
69 | liss (IRC 1C) band 3 (0.612-0.700)
70 | liss (IRC 1C) band 4 (0.752-0.880)
71 | liss (IRC 1C) band 5 (1.452-1.760)
72 | aster band 1 (0.480-0.645)
73 | aster band 2 (0.588-0.733)
74 | aster band 3N (0.723-0.913)
75 | aster band 4 (1.530-1.750)
76 | aster band 5 (2.103-2.285)
77 | aster band 6 (2.105-2.298)
78 | aster band 7 (2.200-2.393)
79 | aster band 8 (2.248-2.475)
80 | aster band 9 (2.295-2.538)
81 | avnir band 1 (0.390-0.550)
82 | avnir band 2 (0.485-0.695)
83 | avnir band 3 (0.545-0.745)
84 | avnir band 4 (0.700-0.925)
85 | ikonos Green band (0.350-1.035)
86 | ikonos Red band (0.350-1.035)
87 | ikonos NIR band (0.350-1.035)
88 | RapidEye Blue band (0.438-0.513)
89 | RapidEye Green band (0.463-0.594)
90 | RapidEye Red band (0.624-0.690)
91 | RapidEye RedEdge band (0.500-0.737)
92 | RapidEye NIR band (0.520-0.862)
93 | VGT1 (SPOT4) band 0 (0.400-0.500)
94 | VGT1 (SPOT4) band 2 (0.580-0.782)
95 | VGT1 (SPOT4) band 3 (0.700-1.030)
96 | VGT1 (SPOT4) MIR band (1.450-1.800)
97 | VGT2 (SPOT5) band 0 (0.400-0.550)
98 | VGT2 (SPOT5) band 2 (0.580-0.780)
99 | VGT2 (SPOT5) band 3 (0.700-1.000)
100 | VGT2 (SPOT5) MIR band (1.450-1.800)
101 | WorldView 2 Panchromatic band (0.447-0.808)
102 | WorldView 2 Coastal Blue band (0.396-0.458)
103 | WorldView 2 Blue band (0.442-0.515)
104 | WorldView 2 Green band (0.506-0.586)
105 | WorldView 2 Yellow band (0.584-0.632)
106 | WorldView 2 Red band (0.624-0.694)
107 | WorldView 2 Red Edge band (0.699-0.749)
108 | WorldView 2 NIR1 band (0.765-0.901)
109 | WorldView 2 NIR2 band (0.856-0.1043)
110 | QuickBird Panchromatic band (0.405-1.053)
111 | QuickBird Blue band (0.430-0.545)
112 | QuickBird Green band (0.466-0.620)
113 | QuickBird Red band (0.590-0.710)
114 | QuickBird NIR1 band (0.715-0.918)
115 | Landsat 8 Coastal Aerosol Band (0.427nm - 0.459nm)
116 | Landsat 8 Blue Band (436nm - 527nm)
117 | Landsat 8 Green Band (512nm-610nm)
118 | Landsat 8 Red Band (625nm-691nm)
119 | Landsat 8 Panchromatic Band (488nm-692nm)
120 | Landsat 8 NIR Band (829nm-900nm)
121 | Landsat 8 Cirrus Band (1340nm-1409nm)
122 | Landsat 8 SWIR1 Band (1515nm - 1697nm)
123 | Landsat 8 SWIR2 Band (2037nm - 2355nm)
115 | GeoEye 1 Panchromatic band (0.450-0.800)
116 | GeoEye 1 Blue Band (0.450-0.510)
117 | GeoEye 1 Green Band (0.510-0.580)
118 | GeoEye 1 Red Band (0.655-0.690)
120 | GeoEye 1 NIR Band (0.780-0.920)

## EXAMPLES

### Atmospheric correction of a LANDSAT-7 channel

The example is based on the North Carolina sample dataset (GMT -5 hours). First we set the computational region to the satellite map, e.g. channel 4:

    g.region raster=lsat7_2002_40 -p

It is important to verify the available metadata for the sun position which has to be defined for the atmospheric correction. An option is to check the satellite overpass time with sun position as reported in the metadata file (file copy; North Carolina sample dataset). In case of the North Carolina sample dataset, values have been stored for each channel and can be retrieved like this:

    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, we can also calculate them from the overpass time as follows (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

If the overpass time is unknown, use the NASA LaRC Satellite Overpass Predictor.

#### Conversion of digital number (DN) to radiance at top-of-atmosphere (TOA)

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

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
    Lλ = ((LMAXλ - LMINλ)/(QCALMAX-QCALMIN)) * (QCAL-QCALMIN) + LMINλ

where:

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

LMINλ and LMAXλ are the radiances related to the minimal and maximal DN value, and are reported in the metadata file for each image, or in the table 1. High gain or low gain is also reported in the metadata file of each satellite image. For Landsat, the minimal DN value (QCALMIN) is 1 for Landsat ETM+ images (see Landsat handbook, see 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:

    CHAN=4
    r.info lsat7_2002_${CHAN}0 -h | tr '\n' ' ' | sed 's+ ++g' | tr ':' '\n' | grep "LMIN_BAND${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 in italics 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.

#### Creation of parameter file for i.atcorr

The underlying 6S model is parametrized through a control file, indicated with the parameter option. This is a text file defining geometrical and atmospherical conditions of the satellite overpass. Below some details:

    # find mean elevation (target above sea level, used as initialization value in control file)
    r.univar elevation

Create a control file 'icnd.txt' for channel 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 mode=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 elev=elevation parameters=icnd_lsat4.txt output=lsat7_2002_40_atcorr

Note that the altitude value from 'icnd_lsat4.txt' file is read at the beginning to compute the initial transform. It is necessary to give a value which could be the mean value of the elevation model. For the atmospheric correction then the raster elevation values are used from the map.

Note that the process is computationally intensive.
Note also, that i.atcorr reports solar elevation angle above horizon rather than solar zenith angle.

## REMAINING DOCUMENTATION ISSUES

1. 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.

## SEE ALSO

GRASS Wiki page about Atmospheric correction

i.aster.toar, i.landsat.toar, r.info, r.mapcalc, r.univar

## REFERENCES

- 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
- Julia A. Barsi, Brian L. Markham and Jeffrey A. Pedelty "The operational land imager: spectral response and spectral uniformity", Proc. SPIE 8153, 81530G (2011); doi:10.1117/12.895438

## AUTHORS

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 Löwe, 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

Last changed: $Date: 2016-05-18 09:07:29 +0200 (Wed, 18 May 2016) $

## SOURCE CODE

Available at: i.atcorr source code (history)

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© 2003-2016 GRASS Development Team, GRASS GIS 7.2.0 Reference Manual