## 4 Results and Discussion  
### Backscattering Simulations  
The backscattering model was applied in its forward mode to simulate the total backscattering coefficients (\(\sigma^{o}_{total}\)) using the surface parameters. Two-thirds of our data points from the field surveys (approximately 65 samples) were used to parameterize the backscattering model. The remaining one-third of the dataset (approximately 32 samples) was used to validate the model. To perform a comprehensive evaluation of modified backscattering model described in Section 3.2.2, the reproduced backscattering was achieved by applying the backscattering model described in Section 3.2.1 and modified backscattering model described in Section 3.2.2, respectively. Figure 2 shows scatterplots that represent the relationship between the SAR measured and predicted backscattering using the backscattering model described in Section 3.2.1 (Figure 2a is the scatterplot for HH polarization and Figure 2b is the scatterplot for VV polarization). The relationship between the measured and predicted backscattering (Figure 2) proved linear, but it had a moderate correlation (\(R^{2}=0.58\), \(p<0.01\) for HH polarization; \(R^{2}=0.52\), \(p<0.01\) for VV polarization). An interesting phenomenon is shown in Figure 2. The higher backscattering zone has larger deviations from the 1:1 lines than the lower backscatteringzone for both HH polarization and VV polarization. In other words, compared to the lower backscattering zone (backscattering less than \(-\)14 dB for HH polarization; backscattering less than \(-\)17 dB for VV polarization), the higher zone showed relatively higher correlations between the predicted and measured backscattering for both HH polarization and VV polarization. In previous studies [44,48], a negative relationship was found between backscattering and vegetation biomass. Therefore, the backscattering decreases with the increase of vegetation biomass. The backscattering decreased with increasing vegetation biomass at low values of the radar signal saturation because the decreasing contributions of soil roughness and moisture impacted the backscattering [49]. In this study, no samples were greater than the saturation level at the C-band (2.0 kg/m\({}^{2}\)) [50]. Considering this claim, it can be assumed that errors in the backscattering model occurred mainly for the plots with relatively low vegetation densities. This phenomenon may be explained by the less-sensitive WCM for relatively sparse vegetation conditions. This view also supports the condition that volume scattering is the dominant mechanism responsible for the backscattering from vegetation when applying the WCM [26]. The backscattering was obtained assuming that the vegetation was homogeneous such that vegetation gap information was not accounted for in the backscattering model described in Section 3.2.1. Thus, the predicted backscattering was less accurate when gaps were present. In view of the sparse and patchy vegetation pattern, the applicability of the assumption was assessed by implementing the assumption only for the relative fraction of vegetation cover and by adding a backscattering component of the complementary soil fraction. The scatterplots demonstrate a strong linear correlation between the measured and predicted backscattering using the modified backscattering model (Figure 3a is the scatterplot for HH polarization and Figure 3b is the scatterplot for VV polarization). The predicted backscattering achieved by the modified backscattering model showed that the correlations between the measured and predicated backscattering was improved by including the effect of bare soil in the entire range of backscattering levels (for HH polarization, \(R^{2}\) increased from 0.58 to 0.79 and the RMSE decreased from 2.04 dB to 1.40 dB; for VV polarization, \(R^{2}\) increased from 0.52 to 0.77 and the RMSE decreased from 2.45 dB to 1.69 dB), especially for the higher backscattering zone (relatively sparse vegetation zone).