Surface Soil Moisture Retrieval Using Sentinel-1 SAR Data for Crop Planning in Kosi River Basin of North Bihar

Abstract

Surface Soil Moisture (SSM) is a key factor for understanding the physical process between the land surface and atmosphere. With the advancement of Synthetic Aperture Radar (SAR) technology and backscattering models, retrieval of SSM over the land surface at higher spatial resolution became effective and accurate. This study examines the potential of C-band Sentinel-1 SAR data to derive SSM in a dry season (February 2020) over bare soil and vegetated agricultural fields in the Kosi River Basin (KRB) in North Bihar. Field campaigns were conducted simultaneously with Sentinel–1A acquisition date, and measurements comprised 54 in-situ sampling plots for the top of the soil (0–7.6 cm depth) using time-domain reflectometry (TDR–300). The modified Dubois model was employed to estimate relative soil permittivity from the backscatter values (σ°) of VV polarization. With the help of Topp’s model, volumetric SSM (m3/m3) was derived for all areas with normalized difference vegetation index (NDVI) less than 0.4 that majorly covered bare land or sparse vegetation. The key findings demonstrated that model-derived SSM was well correlated with the in-situ SSM with the coefficient of determination (R2) of 0.77 and root mean square error (RMSE) of 0.06 m3/m3. The spatial distribution of SSM ranged from 0.05 to 0.5 m3/m3 over the KRB, and the highest moisture was found in the Kosi Megafan. The modified Dubois model was effective in providing SSM from Sentinel–1A data in bare soil and agricultural fields and, thus, supporting use in hydrological, meteorological and crop planning applications.