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 (\(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\)), differences of circular vertical and horizontal \(\sigma ^{\mathrm{o}} \, (\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}})\) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (\({\hbox {RMS}}_{\mathrm{height}}\)). 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., \(\sigma ^{\mathrm{o}}\). Near surface SM measurements were related to \(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\) derived using 5.35 GHz (C-band) image of RISAT-1 and \({\hbox {RMS}}_{\mathrm{height}}\). The roughness component derived in terms of \({\hbox {RMS}}_{\mathrm{height}}\) showed a good positive correlation with \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}} \, (R^{2} = 0.65)\). By considering all the major influencing factors (\(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\), and \({\hbox {RMS}}_{\mathrm{height}}\)), an SEM was developed where SM (volumetric) predicted values depend on \(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\), and \({\hbox {RMS}}_{\mathrm{height}}\). 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 (\({\hbox {SM}}_{\mathrm{Observed}}\)) 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 (\({\approx } 1\)), 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 \(({\hbox {S}}_{\mathrm{d}}^{2}) = 0.004\). The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on \(\sigma ^{\mathrm{o}}\). 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.