volume | PIER Journals

Abstract

Surface soil moisture retrieval in vegetated environments remains a significant challenge because microwave backscattering is affected by soil dielectric properties, vegetation structure, and surface roughness. This study presents an integrated synthetic aperture radar (SAR) and optical framework for retrieving soil moisture by blending C-band Sentinel-1 SAR observations, along with Moderate Resolution Imaging Spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI). The proposed framework normalizes SAR backscatter using the Dubois model and integrates MODIS-derived NDVI via empirical relationships to mitigate vegetation-induced uncertainty while minimizing the dependence on extensive field-based vegetation measurements for soil moisture retrieval. The backscattering coefficient over dry soil was computed using the Dubois empirical model, and it was used to normalize the measured SAR backscatter. Thereafter, empirical relationships were established between the normalized backscatter coefficient and MODIS derived NDVI for different soil moisture ranges using cubic regression models. The developed framework achieved coefficients of determination greater than 0.83 for all empirical relationships. The proposed algorithm for the test dataset and validation dataset resulted in root mean square errors of 0.037 cm³ cm^-3 and 0.043 cm³ cm^-3 respectively. The results of this study signifies the integration of SAR and optical data for soil moisture retrieval in the presence of vegetation.

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Dr. Shivangi Barthwal

Dr. Rishi Prakash

Dr. Anurag Vidyarthi