Tag Archives: AOT

Inverting for Aerosol Optial Thickness (AOT) using ARCSI

Aerosol Optial Thickness (AOT) as an important parameter in atmospheric correction models such as 6S, which is used by the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI) software (see previous post for more details). With time and expert knowledge it would be possible to derive an AOT value for each scene manually. In some regions of the world it has been found that a constant can be used (e.g., Australia use an AOT of 0.05; [1]).

However, for most regions a constant is not viable as the atmosphere is too variable (both temporally and spatially) and not correctly parameterising for AOT can lead to large errors in atmospheric correction [2].

ARCSI provides a method of deriving AOT from an image by using a dark object subtraction (DOS) to estimate surface reflectance in the blue channel. Taking this estimated reflectance as input, 6S is then numerically inverted to identify an AOT value which provides a surface reflectance value as close to that estimated from DOS as possible.

To execute this functionality the following command is used:

arcsi.py --sensor ls8 \
    -i Inputs/LC82040242013139LGN01/LC82040242013139LGN01_MTL.txt \
    -o ./OutputsAOTInv \
    --tmpath ./tmp \
    -f KEA --stats \
    --prodlist RAD DOSAOTSGL SREF \
    --aeroimg ../WorldAerosolParams.kea \
    --atmosimg ../WorldAtmosphereParams.kea \
    --dem ../UKSRTM_90m.kea \
    --minaot 0.05 \
    --maxaot 0.6 --simpledos 

Where:

--sensor ls8
specifies the sensor (in this example Landsat 8)

-i Inputs/LC82040242013139LGN01/LC82040242013139LGN01_MTL.txt
is the header file of the input image.

-o ./Outputs
specifies the output directory where all output file will be saved to.

--tmpath ./tmp
is a directory where temporary files can be outputted during the processing, these will be deleted afterwards.

--f KEA
specifies that the output image file format should be KEA. Any GDAL supported format can be used.

-–stats
specifies that the output images should be populate with statistics and pyramids – makes display much faster (only available for KEA and HFA output formats).

--prodlist RAD TOA SREF
specifies the output products to be generated. You only need to specify what you want, e.g., SREF but if other products (e.g., RAD) are required then these will also be produced even if they are not specified.

--aeroimg ../WorldAerosolParams.kea
is an image file which is sampled to identify the aerosol model to be used for the input image (this can be downloaded from ARCSI downloads).

–atmosimg ../WorldAtmosphereParams.kea
is an image file which is sampled to identify the atmosphere model to be used for the input image (this can be downloaded from ARCSI downloads).

–dem ../UKSRTM_90m.kea
is an elevation model covering the scene, in this case the 90 m SRTM product. The higher the resolution of the DEM available the better. SRTM data are available to download from earthexplorer.

--minaot 0.05
is the lower limit of the AOT values tested when estimating the AOT value to be used to correct the scene.

--maxaot 0.6
is the upper limit of the AOT values tested when estimating the AOT value to be used to correct the scene.

--simpledos
specifies that a simple single valued DOS method should be used to provide an estimate of the Blue SREF used in the inversion.

This command will estimate a value for AOT, use this to parameterise and run 6S to build a LUT at elevation steps of 100 m and apply this to produce surface reflectance (multiplied by a scaling factor of 1000 to reduce the file size).

This post was adapted from notes for a module taught as part of the Masters course in GIS and Remote Sensing at Aberystwyth University, you can read more about the program here.

References
[1] S.S. Gillingham , N. Flood , T.K. Gill. 2013. On determining appropriate aerosol optical depth values for atmospheric correction of satellite imagery for biophysical parameter retrieval: requirements and limitations under Australian conditions; International Journal of Remote Sensing. Vol. 34(6). 2089-2100. http://dx.doi.org/10.1080/01431161.2012.738945
[2] Wilson, R.T., Milton, E.J. & Nield, J.M., 2014. Spatial variability of the atmosphere over southern England, and its effect on scene-based atmospheric corrections. International Journal of Remote Sensing. 35(13). 5198-5218. http://dx.doi.org/10.1080/01431161.2014.939781 (‘Behind the paper’ and pre-print link).

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