Surface soil moisture estimation using dual-Polarimetric Stokes parameters and backscattering coefficient

Abstract

Surface soil moisture (SSM) is an important key aspect highly applied in several fields as water resources, climate change, and agronomy. The soil moisture estimation at high spatial and temporal resolution is considered as an important aspect for a sustainable environment. This study seeks to examine the potential of Stokes parameters and backscattering coefficients of C-band Sentinel-1 to estimate SSM over bare and sparsely vegetated agricultural fields. In this work, a total of 33 surface soil samples (0–5 cm) were collected from 2 provinces in a semi-arid region of northwestern Morocco, the study is conducted between December 24-2020 and February 23-2021, then analyzed in the laboratory to determine the percentage of soil moisture volume. As a second experiment to verify the consistency of our results, a total of 36 surface soil samples (0–5 cm) were collected and measured over a semi-arid area in central India. In addition to the soil samples, the Sentinel-1 data are acquired. In this study, two models are proposed for the estimation of the SSM, namely a model based on partial least squares regression (PLSR) and radar backscatter, and a model based on partial least squares regression (PLSR) and Stokes parameters. The sensitivity of the backscattering coefficients and Stokes parameters of Sentinel-1 was investigated. In the first experiment, the highest sensitivity was obtained by the model based on Stokes parameters and PLSR (R2 = 0.679 and RMSE = 1.96%), for the model that uses the backscattering coefficient and PLSR, we found (R2 = 0.60 and RMSE = 2.17). In the second experiment, the greatest sensitivity was also obtained by the model based on Stokes parameters and PLSR (R2 = 0.49 and RMSE = 4.94%), for the model based on backscattering coefficient and PLSR we found (R2 = 0.39 and RMSE = 5.41). The results showed that the potential of the Stokes and PLSR parameters for estimating the SSM was higher than that of that using the backscattering coefficient and PLSR. Both models showed a significant relationship (p < 0.001) with soil moisture. The results showed that PLSR is a very effective technique for modeling soil moisture.