InSAR4SM is a free and open-source software for estimating soil moisture using interferometric observables. It requires as inputs a) a Topstack ISCE SLC stack and b) a meteorological dataset (e.g. ERA5-Land data). The main output result is a point vector file that contains soil moisture information over time.
This is research code provided to you "as is" with NO WARRANTIES OF CORRECTNESS. Use at your own risk.
The installation notes below are tested only on Linux.
First you have to download InSAR4SM using the following command
git clone https://github.com/kleok/InSAR4SM.git
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
on GNU/Linux, append to .bashrc file:
export InSAR4SM_HOME=~/InSAR4SM
export PYTHONPATH=${PYTHONPATH}:${InSAR4SM_HOME}
export PATH=${PATH}:${InSAR4SM_HOME}
InSAR4SM provide soil moisture estimations using interferometric observables and meteorological data using a 5-step framework.
- 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.
In order to run InSAR4SM please make sure to update/provide the following information located at "Input arguments" cell at InSAR4SM_app.py
# the name of your project
projectname = 'INSAR4SM_estimations_test'
# the directory of the topstack processing stack
topstackDir = '/RSL02/SM_Arabia/Topstack_processing'
# time of Sentinel-1 pass.
orbit_time = '15:00:00'
# the AOI geojson file, ensure that AOI is inside your topstack stack
AOI = '/RSL02/SM_Arabia/aoi/aoi_test.geojson'
# spatial resolution of soil moisture grid in meters
grid_size = 250
# You can set manually a dry date (one of your SAR acquisition dates ) or set to None
dry_date = '20180401'
# set to True in case you provide manually an dry_date
dry_date_manual_flag = True
# the meteorological file. You can either provide an ERA5-land file or a csv file with 3 columns (Datetimes, tp__m, skt__K).
meteo_file = '/RSL02/SM_Arabia/era5/adaptor.mars.internal-1665654570.8663068-23624-3-8bce5925-a7e7-4993-a701-0e05b4e9dabd.nc'
# set to True in case you provide an ERA5-Land file
ERA5_flag = True
# In case you downloaded surface soil moisture from ERA5-land, set to True for comparison purposes
ERA5_sm_flag = True
# soil information datasets (https://soilgrids.org/)
sand_soilgrids = '/RSL02/SM_Arabia/soilgrids/clay.tif'
clay_soilgrids = '/RSL02/SM_Arabia/soilgrids/sand.tif'
# the output directory
export_dir = '/RSL02/SM_Arabia/{}'.format(projectname)
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. Soil moisture estimation from Sentinel-1 interferometric observations over arid regions.(under review). Preprint available here
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!