## 4 Results  
In this study, the proposed new model was used to retrieve soil moisture based on KOMPSAT-5 and Sentinel-1 SAR data. Sixty-five ground soil moisture samples were obtained in the field work. Forty-three samples were used to calculate the empirical coefficients and the remaining samples were applied to evaluate the estimation accuracy of soil moisture. The correlation coefficient (R) and RMSE were used to evaluate the estimated accuracy of soil moisture. In Table 3, the empirical coefficients of the proposed model and [PERSON]'s model were calculated using the ground measurements and SAR data at X- and C-bands. In order to obtain the optimal polarization for soil moisture retrieval, the inversion results from Sentinel-1 VV and VH polarization data were shown in Figure 4. The results demonstrated that the accuracy in VV polarization was obviously higher than that from VH polarization data (VH: R = 0.552, RMSE = 0.035 cm\({}^{3}\)/cm\({}^{3}\); VV: R = 0.706, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Band** & **t1/k1** & **t2/k2** & **t3/k3** & **t4/k4** & **t5/k5** & **k6** & **k7** & **k8** \\ \hline Modified & X & \(-0.2865\) & \(1.1540\) & \(1.0716\) & \(2.1168\) & \(0.1583\) & & & \\ model & C & \(0.4997\) & \(0.8709\) & \(0.1376\) & \(1.5152\) & \(-1.2451\) & & & \\ Bao’s & X & \(3.2115\) & \(-22.8561\) & \(45.9902\) & \(-2.4878\) & \(-49.1541\) & \(0.0667\) & \(-0.5460\) & \(1\) \\ model & C & \(0.2937\) & \(-1.7978\) & \(2.7939\) & \(0.4631\) & \(-2.0030\) & \(0.1416\) & \(-0.7617\) & \(1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Empirical coefficients used in the proposed model and [PERSON]’s model. RMSE = 0.037 cm\({}^{3}\)/cm\({}^{3}\)). Thus, the VV polarization SAR data were used to monitor soil moisture in this study. Meanwhile, in order to obtain the optimal combination method of soil moisture retrieval based on the proposed model, this paper applied the Sentinel-2 optical data to calculate three vegetation indices NDVI, NDWI and CVI, combined with the VV extracted from the KOMPSAT-5 SAR images. Three kinds of inversion combinations composing of three vegetation indices and VV polarization were carried out to evaluate the effectiveness of the semi-empirical model. The comparison results of the soil moisture retrieval accuracy under the three combined methods of the model developed in this paper are shown in Table 4. In the three combined inversion methods, the CVI and VV polarization combination method has the highest R and the lowest RMSE, which is the optimal combination way for the application of the semi-empirical model developed in this paper. The proposed semi-empirical model described in Equation (9) was applied to C- and X-band SAR data to estimate surface soil moisture. In this study, 43 samples were employed as the calibration data to perform the parameterization of the model. The comparison between the simulated soil moisture and the in situ soil moisture is presented in Figure 5. Soil moisture was retrieved based on KOMPSAT-5 at X-band and Sentinel-1 at C-band based on Equation (9). From the estimated result in Figure 5, the estimated accuracy at X-band is better than that at C-band. The RMSEs at X- and C-bands were 0.037 and 0.036 cm\({}^{3}\)/cm\({}^{3}\), and the R were 0.803 and 0.634, respectively. Then, a validation experiment was conducted to further evaluate the accuracy of the proposed model using the other 22 sampling points. As shown in Figure 6, a good linear relationship between the retrieved soil moisture and measurements. The R were 0.862 and 0.616, and the RMSEs were 0.020 cm\({}^{3}\)/cm\({}^{3}\) and 0.032 cm\({}^{3}\)/cm\({}^{3}\) at X-band (Figure 6a) and C-bands (Figure 6c) based on the proposed model, respectively. Compared with [PERSON]'s model at X- (R = 0.819, RMSE = 0.028 cm\({}^{3}\)/cm\({}^{3}\)) in Figure 6b and C-bands (R = 0.680, RMSE = 0.031 cm\({}^{3}\)/cm\({}^{3}\)) in Figure 6d, the proposed model has a significant improvement