The spatiotemporal variation of surface soil moisture is a key subject variable need to be assessed accurately since it plays a crucial role in partitioning of rainfall into runoff and infiltration. Active microwave remote sensing has offered prominent potential towards accurate estimation of surface soil moisture. Present study utilizes ERS-2 SAR image for estimating surface soil moisture by incorporating the effect of topography, vegetation and surface roughness over three land cover types namely; sugarcane, wheat and barren land through Multiple Linear Regression (MLR) approach. Five independent variables considered for MLR analysis include backscatter coefficient (\(σ^0\) ), local incidence angle (\(α_i\)), surface roughness height (\(h_s\)), Leaf Area Index (LAI) and Plant Water Content (PWC), respectively. Results indicate retrieval of soil moisture within ± 20% accuracy for all the three land cover types with higher accuracy for barren land (i.e. R² ~ 0.78 and RMSE = 1.31) as compared to the other two land cover types (i.e. sugarcane; R² ~ 0.67 and RMSE = 3.60 and wheat; R² ~ 0.72 and RMSE = 1.94). The present study reveals the effectiveness of MLR approach in the retrieval of surface soil moisture using fewer numbers of variables.

ERS-2 SAR image has been utilized for estimating surface soil moisture by incorporating the effect of topography, vegetation and surface roughness over three land cover types namely; sugarcane, wheat and barren land through Multiple Linear Regression (MLR) approach.

Five independent variables considered for MLR analysis include backscatter coefficient, local incidence angle, surface roughness height, Leaf Area Index and Plant Water Content, respectively.

Higher accuracy was observed for barren land as compared to the other two land cover types (i.e. sugarcane and wheat).

The present study therefore reveals the effectiveness of MLR approach in the retrieval of surface soil moisture while utilising fewer number of independent variables through microwave remote sensing data.