InSAR4SM - Interferometric Synthetic Aperture Radar for Soil Moisture

Introduction

InSAR4SM is a free and open-source software for estimating soil moisture using interferometric observables over arid regions. It requires as inputs the following data:

• a Topstack ISCE SLC stack
• a meteorological dataset (e.g. from ERA5-Land data)
• a soil texture dataset (sand and clay) for your region of interest (e.g. from soilgrids)

For each Sentinel-1 acqusitions a surface (top 5 cm) soil moisture map is provided.

This is research code provided to you "as is" with NO WARRANTIES OF CORRECTNESS. Use at your own risk.

1. Installation

The installation notes below are tested only on Linux.

1.1 Download InSAR4SM

First you have to download InSAR4SM using the following command

git clone https://github.com/kleok/InSAR4SM.git

1.2 Create python environment for InSAR4SM

InSAR4SM is written in Python3 and relies on several Python modules. You can install them by using INSAR4SM_env.yml file.

conda env create -f INSAR4SM_env.yml

1.3 Set environmental variables (optional)

on GNU/Linux, append to .bashrc file:

export InSAR4SM_HOME=~/InSAR4SM
export PYTHONPATH=${PYTHONPATH}:${InSAR4SM_HOME}
export PATH=${PATH}:${InSAR4SM_HOME}

2. Running InSAR4SM

InSAR4SM provide soil moisture estimations using interferometric observables, meteorological and soil texture data from the following pipeline.

• Identification of driest SAR image based on meteorological information.
• Calculation of interferometric observables (coherence and phase closure).
• Identification of SAR acquisitions related to dry soil moisture conditions using coherence and amplitude information.
• Calculation of coherence information due to soil moisture variations.
• Soil moisture inversion using De Zan`s model.

Please start with the jupyter notebook example here

3. Documentation and citation

Algorithms implemented in the software are described in detail at our publication. If InSAR4SM was useful for you, we encourage you to cite the following work.

• Karamvasis, K., & Karathanassi, V. (2023). Soil moisture estimation from Sentinel-1 interferometric observations over arid regions. Computers & Geosciences, 178, 105410. here

4. Contact us

Feel free to open an issue, comment or pull request. We would like to listen to your thoughts and your recommendations. Any help is very welcome!