Tag Archives: Landsat


At the end of 2014 I used ARCSI (Atmospheric and Radiometric Correction of Satellite Imagery) for the first time. ARCSI is an open software project that provides a command line tool for the atmospheric correction of Earth Observation imagery. It provides a pretty much automatic way of running 6S.

Details of the software can be found here: https://bitbucket.org/petebunting/arcsi

At tutorial can be found here: https://spectraldifferences.wordpress.com/2014/05/27/arcsi/ 

This post is based largely on the tutorial linked to above but I also try to pull together some of the tips I read about in the help forums here: https://groups.google.com/forum/#!forum/rsgislib-support

Set up

All of the following instructions are run on an installation of the Ubuntu 14.04 operating system.

First, download the Anaconda 3.4 python distribution: http://docs.continuum.io/anaconda/

Install the Anaconda version of Python and the conda package manager using the command line. Once the bash script has run then install ARCSI and TuiView (a fast image viewer) using conda. If a warning comes up regarding gdal then install the gdal-data package.

bash Anaconda3-2.1.0-Linux-x86_64.sh

conda install -c https://conda.binstar.org/osgeo arcsi tuiview
conda update rsgislib arcsi

conda install -c jjhelmus gdal-data

For a successful installation I needed to change the default installation path for Anaconda from ~/anaconda3 to ~/anaconda

You should now be able to check your installation using the following command:

arcsi.py -h | less

To finalise the set up you need t point the GDAL driver path to the KEA drivers. Run the following to set up the path:

export GDAL_DRIVER_PATH=~/anaconda/gdalplugins:$GDAL_DRIVER_PATH


export GDAL_DATA

where username is the user account on the Ubuntu installation.

Run the code

When you run ARCSI, you’ll enter a command similar to the following:

arcsi.py -s ls8 -f KEA --stats -p RAD TOA SREF --aeropro NoAerosols --atmospro Tropical --aot 0.25 -o dir/to/outputs -i metadatafile_MTL.txt

To break this down a bit it first calls the arcsi.py script, passing to it the following parameters:

  • Sensor (-s) – landsat 8
  • Output image format (-f) – KEA
  • Parameters to compute (-p) – RAD Radiance conversion, TAO Top of atmosphere, SREF surface reflectance using 6S (for the full range please consult the official documentation)
  • Output directory (-o) – dir/to/outputs, into which the three computed outputs will be saved
  • Information (-i) – Landsat metadata file, using the format provided by the images available through Earth Explorer


Further information of all the parameters can be found in the help forums, the arcsi help command and in the code.

If an error is reported when you first run the ARCSI command, it might be that LIBGFORTRAN.SO.3 cannot be found. This will lead to the 6S model failing. You will need to install the required files using the following command:

sudo apt-get install libgfortran3

Dealing with outputs

The best way to view the output is to start the supplied viewer using the  ‘tuiview‘  command. This is an intuitive to use and very responsive viewer that handles the KEA format natively. To transform the KEA format outputs into a format readable by a wide range of GIS software, use the GDAL Translate command.

gdal_translate -of GTiff keafile.kea outputfile.tif

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Landsat launch

On Feb 11th 2013 the Landsat Data Continuity Mission launches Landsat 8!! This is a hugely important launch as it effectively means that there will be a Landsat archive of comparable imagery running from the 1970s up to the present day. A launch party kit is available here. Landsat 5 was a lifeline after Landsat 6 failed to become functional and Landsat 7 has not been without its hiccups. My fingers are crossed that LDCM/Landsat 8 gets up there safely, gets on-line and starts providing many more amazing images.



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