Dynamic Monitoring of Surface Soil Moisture Fluctuations Using Synthetic Aperture Radar and Data- Driven Algorithms

The primary goal of the study is to employ SAR data and data driven approaches to model Surface Soil Moisture (SSM) for cultivable bare fields. Three experimental test plots were selected which are basically cultivable but due water deficiency the plots are left bare. Samples for surface soil moisture, soil surface roughness and bulk density are collected from test plots in grid sampling manner in parallel with SAR data pass over study area. Sentinel-1A data is pre-processed and each field sampling grid backscattering energy values are obtained. Surface roughness, dielectric constant and backscattered energy were used as input features to model SSM using RF, SVR and BPANN. We observed that BPANN outperformed SVR and RF by accurately predicting soil moisture with RMSE = 0.077, bias = 0.013, and R = 0.94. This study sheds light on small scale agricultural lands which are deficient of water to support crop growth.