Sentinel-1 soil moisture estimation using dual-polarization radar vegetation index and change detection-based approach

This repository outlines a step-by-step workflow for estimating soil moisture at the field scale using Sentinel-1 Ground Range Detected (GRD) Synthetic Aperture Radar (SAR) data. The approach combines the Dual-Polarization Radar Vegetation Index (DpRVIc) with a change detection-based algorithm (CDA) for better accuracy during cropping period. The model showed a correlation coefficient (r) of 0.75 and RMSE of 0.095 m³/m³. For a detailed explanation of the methodology, please refer to the research paper by Bhogapurapu et al. (2022) in Advance in Space Research (https://doi.org/10.1016/j.asr.2022.03.019). Sentinel-1, Soil Moisture, SAR, Synthetic Aperture Radar, Remote Sensing, Google Earth Engine, Change Detection, DpRVI, VV VH Polarization, Agriculture Monitoring, Open Science, Environmental Modeling