Quickstart

Use InSAR4SM_app.py

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)