This python package lowers the entry barrier for accessing and pre-processing Sentinel-1 data for land applications and allows users with little knowledge on SAR and python to produce various Analysis-Ready-Data products.
The Open SAR Toolkit (OST) bundles the full workflow for the generation of Analysis-Ready-Data (ARD) of Sentinel-1 for Land in a single high-level python package. It includes functions for data inventory and advanced sorting as well as downloading from various mirrors. The whole pre-processing is bundled in a single function and different types of ARD can be selected, but also customised. OST does include advanced types of ARD such as combined production of calibrated backscatter, interferometric coherence and the dual- polarimetric H-A-Alpha decomposition. Time-series and multi-temporal statistics (i.e. timescans) can be produced for each of these layers and the generation of sptaially-seamless large-scale mosaic over time is possible a well.
The Open SAR Toolkit realises this by using an object-oriented approach, providing classes for single scene processing, GRD and SLC batch processing routines. The SAR processing itself relies on ESA's Sentinel-1 Toolbox as well as some geospatial python libraries and the Orfeo Toolbox for mosaicking.
You can find examplarotary Jupyter notebooks at https://github.com/ESA-PhiLab/OST_Notebooks for getting started.
A docker image is available from docker hub that contains the full package, including ESA's Sentinel-1 Toolbox, Orfeo Toolbox, Jupyter Lab as well as the Open SAR Toolkit the tutorial notebooks.
Docker installation is possible on various OS. Installation instructions can be found at https://docs.docker.com/install/
After docker is installed and running, launch the container with (adapt the path to the shared host folder):
docker pull buddyvolly/opensartoolkit
docker run -it -p 8888:8888 -v /shared/folder/on/host:/home/ost/shared buddyvolly/opensartoolkit
The docker image automatically executes the jupyter lab and runs it on port 8888. You can find the address to the notebook on the command line where docker is running. Copy it into your favorites browser and replace 127.0.0.1 with localhost.
OST bases mainly on the freely available SNAP toolbox for the SAR-specific processing routines. You can download SNAP from:
http://step.esa.int/main/download/
If you install SNAP into the standard directory, OST should have no problems to find the SNAP command line executable. Otherwise you need to define the path to the gpt file on your own during processing.
If you want to create mosaics between different swaths, OST will rely on the otbcli_Mosaic command from The Orfeo Toolbox. You can download Orfeo from:
https://www.orfeo-toolbox.org/download/
Make sure that the Orfeo bin folder is within your PATH variable to allow execution from command line.
OST is developed under Ubuntu 18.04 OS in python 3.6. It has not been tested much on other OS and python versions, but should in principle work on any OS and any python version >= 3.5.
Before installation of OST, run the following line on the terminal to install further dependencies:
sudo apt install python3-pip git libgdal-dev python3-gdal libspatialindex-dev
then install OST as a global package (for all users, admin rights needed):
sudo pip3 install git+https://github.com/ESA-PhiLab/OpenSarToolkit.git
or as local package within your home folder (no admin rights needed):
pip3 install --user git+https://github.com/ESA-PhiLab/OpenSarToolkit.git
If not already installed, install homebrew as explained on https://brew.sh
After installation of homebrew, open the terminal and install further dependecies:
brew install python3 gdal2 gdal2-python git
then install OST with python pip:
pip3 install git+https://github.com/ESA-PhiLab/OpenSarToolkit.git
Follow the installation instructions for conda (Miniconda is sufficient) at: https://docs.conda.io/projects/conda/en/latest/user-guide/install/
Then run the conda command to install OST's dependencies:
conda install pip gdal jupyter jupyterlab git matplotlib numpy rasterio imageio rtree geopandas fiona shapely matplotlib descartes tqdm scipy joblib retrying pytest pytest-cov
Finally get the OST by using pip (we will work in future on a dedicated conda package for OST).
pip install git+https://github.com/ESA-PhiLab/OpenSarToolkit.git
Year: 2016
Sensor: Sentinel-1 C-Band SAR.
Acquisitions: 6 acquisitions per swath (4 swaths)
Output resolution: 30m
RGB composite:
- Red: VV-maximum
- Green: VV-minimum
- Blue: VV-Standard deviation
Year: 2016-2017
Sensor: Sentinel-1 C-Band SAR.
Acquisitions: 7 acquisitions per swath (about 400 scenes over 8 swaths)
Output resolution: 30m
RGB composite:
- Red: VV-minimum
- Green: VH-minimum
- Blue: VV-Standard deviation
Open SAR Toolkit was initially developed at the Food and Agriculture Organization of the United Nations under the SEPAL project (https://github.com/openforis/sepal) between 2016-2018. It is still available there (https://github.com/openforis/opensarkit), but has been completely re-factored and transferred into a simpler and less-dependency rich python3 version, which can be found on this page here. Instead of using R-Shiny as a GUI, the main interface are now Jupyter notebooks that are developed in parallel to this core package and should help to get started. (https://github.com/ESA-PhiLab/OST_Notebooks)
- Andreas Vollrath, ESA
- Petr Sevcik, EOX