# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 3 Results and Discussion
### Downscaling Results  
#### 3.2.1 Comparison Before and After Downscaling  
The downscaling results for the Shandian River and Wudaoliang (Figures 5-7) indicate that the textures of the soil moisture values before and after downscaling are consistent. The downscaled image can provide more detailed spatial details, and the jaggedness is significantly reduced, especially after adding SAR backscattering coefficient data as features [2; 18]. The soil moisture content is lowest in the northern part of the Shandian River and highest in the southwestern part, increasing from northeast to southwest. Combined with the land cover type of the study area, the southern part is forested, with a high vegetation water storage capacity and high soil moisture content. Owing to the obstruction of the vegetation canopy, the accuracy of the soil moisture inversion decreases, which may lead to a lower accuracy of the soil moisture verification results [7]; in the northern part, there is a large area of grassland. In comparison, the water storage capacity of grasslands is not as strong as that of forests; it is not blocked by the vegetation canopy and is more suitable for microwave detection. The results for Wudaoliang are more impressive, with consistent changes before and after downscaling. An investigation of the study area revealed that grassland covers most of the land surface, with a very small amount of mixed desert and no forest or trees blocking it. The surface type is relatively uniform, which improves the soil moisture downscaling effect. The comparison results of the maximum, minimum, and average values of the images before and after downscaling are shown in Table 4. As can be seen from it, the minimum difference of the pixels before and after downscaling is 0.017 (cm\({}^{3}\)/cm\({}^{3}\)), the maximum difference is 0.013 (cm\({}^{3}\)/cm\({}^{3}\)), and the average difference is 0.018 (cm\({}^{3}\)/cm\({}^{3}\)). The range of the downscaled soil moisture values is smaller than the range of the original soil moisture. High values are underestimated, and low values are overestimated [5; 18]. This may be due to insufficient training samples, the model may have relatively weak prediction ability for these values, or it may be caused by overfitting. However, in general, the deviations between the original soil moisture and the downscaled data are within an acceptable range. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Min (cm\({}^{3}\)/cm\({}^{3}\))** & **Max (cm\({}^{3}\)/cm\({}^{3}\))** & **Mean (cm\({}^{3}\)/cm\({}^{3}\))** \\ \hline Before downscaling & 0.120 & 0.313 & 0.245 \\ \hline After downscaling & 0.137 & 0.300 & 0.227 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the maximum and mean values of the image before and after downscaling in the Shandian River. Figure 4: Various feature weights of RF. ALB, LAI, LST, NDVI, SLOPE, VV, and VH are auxiliary data used in building downscaling models using the random forest algorithm. Figure 5: Soil moisture distributions in the Shandian River before and after downscaling. SMAP_10 km is the original soil moisture; SMAP_NOVV_1 km is the downscaled soil moisture without SAR backscattering coefficient data; SMAP_1 km and SMAP_100m are the downscaled soil moisture with added SAR backscattering coefficient data. Figure 6: Soil moisture distributions in the Shandian River before and after downscaling. SMAP_10 km is the original soil moisture; SMAP_NOVV_1 km is the downscaled soil moisture without SAR backscattering coefficient data; SMAP_1 km and SMAP_100m are the downscaled soil moisture with added SAR backscattering coefficient data. Furthermore, we quantified the changes in soil moisture values before and after downscaling. According to the scatter plots and Taylor diagram of the correlation analysis of image pixel values before and after downscaling (Figures 8 and 9), it can be seen that in the Shandian River, the R ranges from 0.46 to 0.65, and the central root mean square error ranges from 0.02 to 0.03 (cm\({}^{3}\)/cm\({}^{3}\)).