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In past studies, several researchers took potential use of multi-temporal optical data and dual-polarized SAR data to assess drought by estimating soil moisture. In this study, Modified Dubois Model (MDM) semi-empirical model with Topp's model is used for retrieval of soil moisture. It involves retrieving the backscattering coefficient from RISAT-1 and SENTINEL-1 datasets to derive the surface roughness and soil moisture conditions. The estimated soil moisture retrieved from microwave SAR parameters is validated with field measurements provides soil moisture spatial variability over different land use classes and bare soil condition. The RISAT-1 derived soil moisture has R 2  = 0.53, whereas SENTINEL-1 shows R 2  = 0.84. It also confirms the possibility of two different polarization σ° HH  and σ° VV  backscatter involving MDM. It observes that SENTINEL-1 was found well correlated with ground-measured soil moisture. Also, the averaged NDVI sounds reliable with soil moisture ratio, which helps to understand the impact of agricultural drought monitoring.

Soil moisture estimation using RISAT-1 and SENTINEL-1 data using modified Dubois model in comparison with averaged NDVI