# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 3 Results and Discussion
### Downscaling Results
#### 3.2.2 Validation of Downscaled SMAP and In Situ SM  
In this study, data from 34 stations in the Shandian River and 10 stations in Wudaoliang were used to verify the downscaling results. The results of the correlation analysis of the original soil moisture, the downscaled data, and in situ SM at the spatial scale are shown in Tables 5 and 6. In the Shandian River, the average R of SMAP_10 km, SMAP_NOVV_1 km, and SMAP_1 km with in situ SM are 0.19, 0.27, 0.39, and 0.40, respectively. The RMSE of the Shandian River ranges from 0.031 to 0.091 (cm\({}^{3}\)/cm\({}^{3}\)). The R of the original soil moisture, downscaled data, and in situ SM in Wudaoliang are \(-\)0.59, 0.54, and 0.70, respectively. The RMSE of Wudaoliang ranges from 0.030 to 0.041 (cm\({}^{3}\)/cm\({}^{3}\)).  
The correlation between SMAP_NOVV_1 km and in situ SM has not improved much, especially in the Shandian River; however, the correlation between SMAP_1 km and in situ SM has improved significantly, with average R values of 0.42 and 0.70 in the two places, respectively, and an average R of SMAP_100m and in situ SM of 0.45. The first law of geography holds that geospatial data usually have a property referred to as spatial autocorrelation; that is, data that are spatially close are strongly correlated [39; 40]. Coexisting with spatial autocorrelation is spatial heterogeneity, which varies by location. The existence of spatial heterogeneity causes different sources of interference with remote sensing image pixel values. The ability to detect and interpret spatial heterogeneity depends on the resolution, and the content of the pixels also depends on the spatial resolution of the remote sensing data. Therefore, when its resolution is coarser, the inaccuracy may be larger [41; 42; 43].  
Figure 9: Comparison of Taylor diagrams before and after downscaling in the Wudaoliang area. P1 represents 20/08/12, and P2 represents 20/08/20.  
\begin{table}
\begin{tabular}{c 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**} \\ \cline{2-10}  & \multicolumn{3}{c}{**2019**} & \multicolumn{6}{c}{**2020**} & \multicolumn{6}{c}{**2020**} \\ \cline{2-10}  & **7** & **8** & **9** & **10** & **7** & **8** & **9** & **10** & **08.12** & **08.20** \\ \hline SMAP\_10 km & 0.17 & 0.20 & 0.38 & \(-\)0.13 & 0.26 & 0.15 & 0.29 & 0.22 & \(-\)0.6 & \(-\)0.57 \\ SMAP\_NOVV\_1 km & 0.38 & 0.27 & 0.32 & \(-\)0.12 & 0.24 & 0.27 & 0.42 & 0.38 & 0.46 & 0.61 \\ SMAP\_1 km & 0.59 & 0.42 & 0.45 & 0.24 & 0.18 & 0.35 & 0.48 & 0.41 & 0.64 & 0.75 \\ SMAP\_100m & 0.44 & 0.45 & 0.53 & 0.28 & 0.54 & 0.39 & 0.25 & 0.33 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correlations between remote sensing images and in situ observation (R).  
To further explore the effect of downscaling, we presented it in the form of scatter plots, as shown in Figure 10. Figure 10,b are scatter plots of the soil moisture image and the in situ SM before and after downscaling in the Shandian River, and R and RMSE values were calculated. The value distribution after downscaling is closer to the 1:1 straight line than before downscaling, with an R of 0.41, which is greater than 0.34 before downscaling, and an RMSE of 0.03 (cm\({}^{3}\)/cm\({}^{3}\)), which is consistent with the value before downscaling.  
This result is similar to the verification results of [PERSON] [12; 15; 32; 38; 44], which show that the correlation between soil moisture obtained by the site and remote sensing technology is not very high in terms of spatial scale, but the downscaled soil moisture is closer to in situ SM than the original soil moisture. This further proves the importance of SAR backscatter coefficient data in soil moisture downscaling research and also shows that the new dataset is better than the original SMAP data.