## 4 Discussion  
The accuracy of the three models of the downscaling factor model of the _TVDI_, original _SMI_, and regression model were verified through four 9 km \(\times\) 9 km sampling areas. Table 7 shows that the mean RMSE of the downscaling factor model of the _TVDI_ was slightly lower than that of the original _SMI_ method, whereas the RMSE of the regression model was higher than the previous two, indicating that the overall effect of the downscaling factor model of the _TVDI_ and the original _SMI_ method  
Figure 6: Time series (May 2016–May 2017) of SMAP soil moisture after downscaling using the _TVDI_ in four sample areas.  
Figure 7: Spatial and temporal distribution of SMAP soil moisture after downscaling in the study area.  
was close. According to the accuracy of the four sampling areas, the accuracy of the downscaling factor model in A1 and A2 was higher than that of the original _SMI_ method. However, for B1 and B2, the accuracy of the original _SMI_ method was higher. The types of sampling areas corresponding to A1 and A2 were wetlands and cities, implying that the downscaling factor model of the _TVDI_ was more suitable for areas with low vegetation coverage. B1 and B2 corresponded to the sampling areas of forest and cultivated land, making them more suitable for the original _SMI_ method. The regression model was simpler and easier to implement, but it was less accurate than the other two methods. The R\({}^{2}\) of the regression model downscaling results was lower than that of the downscaling method, indicating that the estimated value after downscaling was closer to the observed value, but the degree of model fit was reduced.  
The experimental error of wetland and grassland was greater than that of other land-cover types. The second reason for the error distribution involved the complexity of land-cover types. The A2 area contained urban, cropland, and mixed forests, and the land-cover type was more than B2 (mainly cultivated land); accordingly, the error of A2 was greater than that of B2. Additionally, the existence of mixed pixels was a cause of error, as evidenced by the error in the SMAP grid scale being greater overall than that in the sampling scale. Thus, the land-cover type appeared to be one of the impressive factors in the downscaling treatment, and the influence of wetland and cultivated land was more obvious (Figure 8). The downscaled results generally improved the description of spatial differentiation patterns, and can be applied to hydrological studies at the watershed scale.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Area** & \begin{tabular}{c} **Downscaling Soil** \\ **Moisture Using _TVDI_** \\ \end{tabular} & \begin{tabular}{c} **Original _SMI_** \\ **Method** \\ \end{tabular} & \
\begin{tabular}{c} **Regression Model** \\ \end{tabular} &
\begin{tabular}{c} **Level 3 SMAP SOIL** \\ **MOISTURE at 9-km** \\ **Resolution** \\ \end{tabular} \\ \cline{2-6}  & **RMSE** & **RMSE** & **RMSE** & **R\({}^{2}\)** & \\ \hline A1 & 0.0112 & 0.0138 & 0.0150 & 0.4752 & 0.0260 \\ A2 & 0.0218 & 0.0223 & 0.0257 & 0.5387 & 0.0640 \\ B1 & 0.0156 & 0.0190 & 0.0209 & 0.5095 & 0.0555 \\ B2 & 0.0081 & 0.0119 & 0.0146 & 0.5291 & 0.0341 \\ Mean = & 0.0142 & 0.0167 & 0.0190 & 0.5131 & 0.0449 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of accuracy verification effect of soil moisture downscaling model.  
Figure 8: Land-cover reference and downscaling results of soil moisture. (**A**) Land cover types in the study area; (**B**) Soil moisture at 0.3-km resolution after downscaling using the _TVDI_ as the downscaling factor.