# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 3 Results and Discussion
### Downscaling Results
#### 3.2.3 Comparison with Previous Studies  
Previous soil moisture downscaling studies had proposed a variety of downscaling methods and achieved good results, such as the triangular/trapezoidal method, the semi-physical DISPATCH method, polynomial fitting, machine learning, and deep learning [17; 45; 46]. The selection of auxiliary datasets was mainly based on MODIS visible light/near-infrared data. In addition to the LST, NDVI, and topographic factors, some studies used rainfall as auxiliary data [1; 22; 47], but a few methods included the backscatter coefficient of microwave remote sensing [48; 49].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**SDR In Situ SM**} & \multicolumn{6}{c}{**WDL In Situ SM**} \\ \hline  & \multicolumn{3}{c}{**2019**} & \multicolumn{3}{c}{**2020**} & \multicolumn{3}{c}{**2020**} \\ \cline{2-10}  & **7** & **8** & **9** & **10** & **7** & **8** & **9** & **10** & **08.12** & **08.20** \\ \hline SMAP\_10 km & 0.035 & 0.082 & 0.030 & 0.091 & 0.060 & 0.069 & 0.069 & 0.066 & 0.040 & 0.041 \\ SMAP\_NOV\_1 km & 0.034 & 0.079 & 0.035 & 0.089 & 0.060 & 0.069 & 0.066 & 0.061 & 0.036 & 0.031 \\ SMAP\_1 km & 0.031 & 0.074 & 0.035 & 0.086 & 0.062 & 0.068 & 0.065 & 0.060 & 0.030 & 0.035 \\ SMAP\_100m & 0.033 & 0.074 & 0.032 & 0.088 & 0.055 & 0.064 & 0.070 & 0.064 & & \\ \hline \hline \end{tabular}  
* Notes: SMAP\_10 km is the original soil moisture; SMAP\_NOV\_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.  
\end{table}
Table 6: Difference between remote sensing images and in situ observation (RMSE (cm\({}^{3}\)/cm\({}^{3}\))).  
Figure 10: Scatter plots of soil moisture and in situ SM before and after downscaling. (**a**) is the verification result before downscaling; (**b**) is the verification result after downscaling. The red dotted line in the figure indicates the 1:1 line.  
The research results showed that microwave remote sensing can penetrate the surface, and SAR backscattering coefficients are very sensitive to the dielectric properties of surface soil moisture, which is very beneficial for exploring the spatial distribution of soil moisture [27; 49; 50]. Based on the conclusions of previous studies, this study considered using SAR backscattering coefficient data as an important characteristic variable to downscale soil moisture, analyzed the difference between the results of downscaling based on fused data and optical data alone, and verified that the fused data play an important role in downscaling.  
The results of previous studies are summarized in Table 7. A comparison between Table 7 and the findings of this study reveals that the verification results for spatial scale indicate that the R values mostly fall between 0 and 0.6, while the RMSE value ranges from 0.01 to 0.141 cm\({}^{3}\)/cm\({}^{3}\). Notably, the R value from the verification results of this study is slightly higher, and the RMSE value is slightly lower than the values reported in previous conclusions.