i.landsat.toar(1)

📦
version 8.4.2 · 8 versions
, in fedora 44

✨ i.landsat.toar(1) — GRASS GIS User's Manual 💅 (part 1 of 2)

okay bestie, gather round, because this is the manual page for i.landsat.toar and it is a lot, but I got you. Part one, let's go. 🫶

🌸 NAME

i.landsat.toar — Calculates top-of-atmosphere radiance or reflectance and temperature for Landsat MSS/TM/ETM+/OLI

🎀 KEYWORDS

imagery, radiometric conversion, radiance, reflectance, brightness temperature, atmospheric correction, satellite, Landsat

✨ SYNOPSIS

i.landsat.toar
i.landsat.toar --help
i.landsat.toar [-rnp] input=basename output=basename  [metfile=name]
[sensor=string]   [method=string]   [date=yyyy-mm-dd]
[sun_elevation=float]   [product_date=yyyy-mm-dd]   [gain=string]
[percent=float]   [pixel=integer]   [rayleigh=float]
[lsatmet=string[,string,...]]   [scale=float]   [--overwrite]  [--help]
[--verbose]  [--quiet]  [--ui]

🚩 Flags

  • -r — Output at-sensor radiance instead of reflectance for all bands
  • -n — Input raster maps use as extension the number of the band instead the code
  • -p — Print output metadata info
  • --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=basename [required] — Base name of input raster bands
    • Example: B. for B.1, B.2, ...
  • output=basename [required] — Prefix for output raster maps
    • Example: B.toar. generates B.toar.1, B.toar.2, ...
  • metfile=name — Name of Landsat metadata file (.met or MTL.txt)
  • sensor=string — Spacecraft sensor
    • Required only if metfile not given (recommended for sanity)
    • Options: mss1, mss2, mss3, mss4, mss5, tm4, tm5, tm7, oli8
    • mss1: Landsat-1 MSS
    • mss2: Landsat-2 MSS
    • mss3: Landsat-3 MSS
    • mss4: Landsat-4 MSS
    • mss5: Landsat-5 MSS
    • tm4: Landsat-4 TM
    • tm5: Landsat-5 TM
    • tm7: Landsat-7 ETM+
    • oli8: Landsat_8 OLI/TIRS
  • method=string — Atmospheric correction method
    • Options: uncorrected, dos1, dos2, dos2b, dos3, dos4
    • Default: uncorrected
  • date=yyyy-mm-dd — Image acquisition date (yyyy-mm-dd)
    • Required only if metfile not given
  • sun_elevation=float — Sun elevation in degrees
    • Required only if metfile not given
  • product_date=yyyy-mm-dd — Image creation date (yyyy-mm-dd)
    • Required only if metfile not given
  • gain=string — Gain (H/L) of all Landsat ETM+ bands (1-5,61,62,7,8)
    • Required only if metfile not given
  • percent=float — Percent of solar radiance in path radiance
    • Required only if method is any DOS
    • Default: 0.01
  • pixel=integer — Minimum pixels to consider digital number as dark object
    • Required only if method is any DOS
    • Default: 1000
  • rayleigh=float — Rayleigh atmosphere (diffuse sky irradiance)
    • Required only if method is DOS3
    • Default: 0.0
  • lsatmet=string[,string,...] — return value stored for a given metadata
    • Required only if metfile and -p given
    • Options: number, creation, date, sun_elev, sensor, bands, sunaz, time
    • number: Landsat Number
    • creation: Creation timestamp
    • date: Date
    • sun_elev: Sun Elevation
    • sensor: Sensor
    • bands: Bands count
    • sunaz: Sun Azimuth Angle
    • time: Time
  • scale=float — Scale factor for output
    • Default: 1.0

💖 DESCRIPTION

i.landsat.toar is the glow-up module: it takes the calibrated digital number of Landsat imagery products and transforms it into top-of-atmosphere radiance or top-of-atmosphere reflectance and temperature (that's band 6 of the sensors TM and ETM+, bestie, the thermal one 🔥). Optionally, it can also calculate the at-surface radiance or reflectance with atmospheric correction (the DOS method). Iconic.

Usually, to do so the production date, the acquisition date, and the solar elevation are needed. Moreover, for Landsat-7 ETM+ it is also needed the gain (high or low) of the nine respective bands. Yes, all of that, no skipping. 😌

Optionally (recommended), the data can be read from metadata file (.met or MTL.txt) for all Landsat MSS, TM, ETM+ and OLI/TIRS. However, if the solar elevation is given the value of the metadata file is overwritten. This is necessary when the data in the .met file is incorrect or not accurate. Also, if acquisition or production dates are not found in the metadata file then the command line values are used.

⚠️ Attention: Any null value or smaller than QCALmin in the input raster is set to null in the output raster and it is not included in the equations. Not invited to the math party, literally.
⚠️ Attention: This module does not respect the current region settings, in order to have the largest possible sample of pixels from where to get the darkest one of the scene and perform the DOS correction. To limit the results to a custom region, the user is advised to clip the results (with r.clip, for instance) or to define the region first, import the images with region cropping, and then running the module. So do your region homework before, bestie.

🌈 Uncorrected at-sensor values (method=uncorrected, default)

The standard geometric and radiometric corrections result in a calibrated digital number (QCAL = DN) images. To further standardize the impact of illumination geometry, the QCAL images are first converted first to at-sensor radiance and then to at-sensor reflectance. The thermal band is first converted from QCAL to at-sensor radiance, and then to effective at-sensor temperature in Kelvin degrees.

Radiometric calibration converts QCAL to at-sensor radiance, a radiometric quantity measured in W/(m² * sr * µm) using the equations:

  • gain = (Lmax - Lmin) / (QCALmax - QCALmin)
  • bias = Lmin - gain * QCALmin
  • radiance = gain * QCAL + bias

where, Lmax and Lmin are the calibration constants, and QCALmax and QCALmin are the highest and the lowest points of the range of rescaled radiance in QCAL.

Then, to calculate at-sensor reflectance the equations are:

  • sun_radiance = [Esun * sin(e)] / (PI * d^2)
  • reflectance = radiance / sun_radiance

where, d is the earth-sun distance in astronomical units, e is the solar elevation angle, and Esun is the mean solar exoatmospheric irradiance in W/(m² * µm).

🦋 Simplified at-surface values (method=dos[1-4])

Atmospheric correction and reflectance calibration remove the path radiance, i.e. the stray light from the atmosphere, and the spectral effect of solar illumination. To output these simple at-surface radiance and at-surface reflectance, the equations are (not for thermal bands):

  • sun_radiance = TAUv * [Esun * sin(e) * TAUz + Esky] / (PI * d^2)
  • radiance_path = radiance_dark - percent * sun_radiance
  • radiance = (at-sensor_radiance - radiance_path)
  • reflectance = radiance / sun_radiance

where, percent is a value between 0.0 and 1.0 (usually 0.01), Esky is the diffuse sky irradiance, TAUz is the atmospheric transmittance along the path from the sun to the ground surface, and TAUv is the atmospheric transmittance along the path from the ground surface to the sensor. radiance_dark is the at-sensor radiance calculated from the darkest object, i.e. DN with a least dark_parameter (usually 1000) pixels for the entire image. The values are,

  • DOS1: TAUv = 1.0, TAUz = 1.0 and Esky = 0.0
  • DOS2: TAUv = 1.0, Esky = 0.0, and TAUz = sin(e) for all bands with maximum wave length less than 1. (i.e. bands 4-6 MSS, 1-4 TM, and 1-4 ETM+) other bands TAUz = 1.0
  • DOS3: TAUv = exp[-t/cos(sat_zenith)], TAUz = exp[-t/sin(e)], Esky = rayleigh
  • DOS4: TAUv = exp[-t/cos(sat_zenith)], TAUz = exp[-t/sin(e)], Esky = PI * radiance_dark
⚠️ Attention: Output radiance remain untouched (i.e. no set to 0.0 when it is negative) then they are possible negative values. However, output reflectance is set to 0.0 when is obtained a negative value. So radiance can go negative and just vibe there, reflectance cannot. 💀

🔥 NOTES

The output raster cell values can be rescaled with the scale parameter (e.g., with 100 in case of using reflectance output in i.gensigset). Cute, flexible, we love.

👑 On Landsat-8 metadata file

NASA reports a structure of the L1G Metadata file (LDCM-DFCB-004.pdf) for Landsat Data Continuity Mission (i.e. Landsat-8).

NASA retains in MIN_MAX_RADIANCE group the necessary information to transform Digital Numbers (DN) in radiance values. Then, i.landsat.toar replaces the possible standard values with the metadata values. The results match with the values reported by the metada file in RADIOMETRIC_RESCALING group.

Also, NASA reports the same values of reflectance for all bands in max-min values and in gain-bias values. This is strange that all bands have the same range of reflectance. Also, they wrote in the web page as to calculate reflectance directly from DN, first with RADIOMETRIC_RESCALING values and second divided by sin(sun_elevation).

This is a simple rescaling

  • reflectance = radiance / sun_radiance = (DN * RADIANCE_MULT + RADIANCE_ADD) / sun_radiance
  • now reflectance = DN * REFLECTANCE_MULT + REFLECTANCE_ADD
  • then REFLECTANCE_MULT = RADIANCE_MULT / sun_radiance
  • and REFLECTANCE_ADD = RADIANCE_ADD / sun_radiance

The problem arises when we need ESUN values (not provided) to compute sun_radiance and DOS. We assume that REFLECTANCE_MAXIMUM corresponds to the RADIANCE_MAXIMUM, then

  • REFLECTANCE_MAXIMUM / sin(e) = RADIANCE_MAXIMUM / sun_radiance
  • Esun = (PI * d^2) * RADIANCE_MAXIMUM / REFLECTANCE_MAXIMUM

where d is the earth-sun distance provided by metadata file or computed inside the program.

The i.landsat.toar reverts back the NASA rescaling to continue using Lmax, Lmin, and Esun values to compute the constant to convert DN to radiance and radiance to reflectance with the "traditional" equations and simple atmospheric corrections.

⚠️ Attention: When MAXIMUM values are not provided, i.landsat.toar tries to calculate Lmax, Lmin, and Esun from RADIOMETRIC_RESCALING (in tests the results were the same).

⭐ Calibration constants

In verbose mode (flag --verbose), the program write basic satellite data and the parameters used in the transformations. It's giving receipts.

Production date is not an exact value but it is necessary to apply correct calibration constants, which were changed in the dates:

  • Landsat-1 MSS: never
  • Landsat-2 MSS: July 16, 1975
  • Landsat-3 MSS: June 1, 1978
  • Landsat-4 MSS: August 26, 1982 and April 1, 1983
  • Landsat-4 TM: August 1, 1983 and January 15, 1984
  • Landsat-5 MSS: April 6, 1984 and November 9, 1984
  • Landsat-5 TM: May 4, 2003 and April, 2 2007
  • Landsat-7 ETM+: July 1, 2000
  • Landsat-8 OLI/TIRS: launched in 2013

✨ EXAMPLES ✨

📁 Metadata file examples

So like, say you wanna transform digital numbers of Landsat-7 ETM+ in band rasters 203_30.1, 203_30.2 [...] into uncorrected at-sensor reflectance in output files 203_30.1_toar, 203_30.2_toar [...], plus at-sensor temperature in output files 293_39.61_toar and 293_39.62_toar — here's the move, bestie:

i.landsat.toar input=203_30. output=_toar \
  metfile=p203r030_7x20010620.met

or

i.landsat.toar input=L5121060_06020060714. \
  output=L5121060_06020060714_toar \
  metfile=L5121060_06020060714_MTL.txt

or

i.landsat.toar input=LC80160352013134LGN03_B output=toar \
  metfile=LC80160352013134LGN03_MTL.txt sensor=oli8 date=2013-05-14

🎀 DOS1 example

DN to reflectance using DOS1, let's goooo:

# rename channels or make a copy to match i.landsat.toar’s input scheme:
g.copy raster=lsat7_2002_10,lsat7_2002.1
g.copy raster=lsat7_2002_20,lsat7_2002.2
g.copy raster=lsat7_2002_30,lsat7_2002.3
g.copy raster=lsat7_2002_40,lsat7_2002.4
g.copy raster=lsat7_2002_50,lsat7_2002.5
g.copy raster=lsat7_2002_61,lsat7_2002.61
g.copy raster=lsat7_2002_62,lsat7_2002.62
g.copy raster=lsat7_2002_70,lsat7_2002.7
g.copy raster=lsat7_2002_80,lsat7_2002.8

Calculation of reflectance values from DN using DOS1 (metadata obtained from p016r035_7x20020524.met.gz):

i.landsat.toar input=lsat7_2002. output=lsat7_2002_toar. sensor=tm7 \
  method=dos1 date=2002-05-24 sun_elevation=64.7730999 \
  product_date=2004-02-12 gain=HHHLHLHHL

The resulting Landsat channels are named lsat7_2002_toar.1 .. lsat7_2002_toar.8. 🦋

📚 REFERENCES 📚

  • Chander G., B.L. Markham and D.L. Helder, 2009: Remote Sensing of Environment, vol. 113
  • Chander G.H. and B. Markham, 2003: IEEE Transactions On Geoscience And Remote Sensing, vol. 41, no. 11.
  • Chavez P.S., jr. 1996: Image-based atmospheric corrections - Revisited and Improved. Photogrammetric Engineering and Remote Sensing 62(9): 1025-1036.
  • Huang et al: At-Satellite Reflectance, 2002: A First Order Normalization Of Landsat 7 ETM+ Images.
  • R. Irish: Landsat 7. Science Data Users Handbook. February 17, 2007; 15 May 2011.
  • Markham B.L. and J.L. Barker, 1986: Landsat MSS and TM Post-Calibration Dynamic Ranges, Exoatmospheric Reflectances and At-Satellite Temperatures. EOSAT Landsat Technical Notes, No. 1.
  • Moran M.S., R.D. Jackson, P.N. Slater and P.M. Teillet, 1992: Remote Sensing of Environment, vol. 41.
  • Song et al, 2001: Classification and Change Detection Using Landsat TM Data, When and How to Correct Atmospheric Effects? Remote Sensing of Environment, vol. 75.

👀 SEE ALSO 👀

i.atcorr, i.colors.enhance, r.mapcalc, r.in.gdal

Landsat Data Dictionary by USGS

👑 AUTHOR 👑

E. Jorge Tizado (ej.tizado unileon es), Dept. Biodiversity and Environmental Management, University of León, Spain

💻 SOURCE CODE 💻

Available at: i.landsat.toar source code (history)

Accessed: Saturday Jan 17 05:41:41 2026

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© 2003-2025 GRASS Development Team, GRASS GIS 8.4.2 Reference Manual

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