Provided by: grass-doc_7.0.3-1build1_all 
NAME
i.atcorr - Performs atmospheric correction using the 6S algorithm.
6S - Second Simulation of Satellite Signal in the Solar Spectrum.
KEYWORDS
imagery, atmospheric correction
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. *
* 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)
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 metadata. For the North Carolina sample dataset, they
have also 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 Satellite Overpass Predictor.
Convert DN (digital number = pixel values) to Radiance at top-of-atmosphere (TOA), using
the formula
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 Landsat image. 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)"
# 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
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
Last changed: $Date: 2015-11-12 15:44:10 +0100 (Thu, 12 Nov 2015) $
Main index | Imagery index | Topics index | Keywords index | Full index
© 2003-2016 GRASS Development Team, GRASS GIS 7.0.3 Reference Manual