We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (σ^o_RH), differences of circular vertical and horizontal σ^o (σ^o_RV− σ^o _RH) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (RMS_height). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., σ^o. Near surface SM measurements were related to σ^o_RH, σ^o_RV−σ^o_RH derived using 5.35 GHz (C-band) image of RISAT-1 and RMS_height. The roughness component derived in terms of RMS_height showed a good positive correlation with σ^o_RH−σ^o_RH (R₂ = 0.65). By considering all the major influencing factors (σ^o_RH, σ^o_RV− σ^o_RH, and RMS_height), an SEM was developed where SM (volumetric) predicted values depend on σ^o_RH, σ^o_RV− σ^o_RH, and RMS_height. This SEM showed R² of 0.87 and adjusted R² of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement (SM_Observed) showed root mean square error (RMSE) = 0.06, relative- RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash–Sutcliffe efficiency (NSE) = 0.91 (≈1), index of agreement (d) = 1, coefficient of determination (R₂) = 0.87, mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences (S²_d) = 0.004. The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on σ^o. By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.