Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (so RH), differences of circular vertical and horizontal sigma(o) (sigma(o)(RV)-sigma(o)(RH)) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (RMSheight). 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., sigma(o). Near surface SM measurements were related to sigma(o)(RH), sigma(o)(RV)-sigma(o)(RH) derived using 5.35 GHz (C-band) image of RISAT-1 and RMSheight. The roughness component derived in terms of RMSheight showed a good positive correlation with sigma(o)(RV)-sigma(o)(RH) (R-2 = 0.65). By considering all the major influencing factors (sigma(o)(RH), sigma(o)(RV)-sigma(o)(RH), and RMSheight), an SEM was developed where SM (volumetric) predicted values depend on sigma(o)(RH), so RV-sigma(o)(RH), and RMSheight. This SEM showed R-2 of 0.87 and adjusted R-2 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 (SMObserved) 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 (approximate to 1), index of agreement (d) = 1, coefficient of determination (R-2) = 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 (S-d(2)) = 0.004. The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on sigma(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.