# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 4 Conclusions  
This study used machine learning algorithms to effectively downscale soil moisture and discussed the differences between downscaling using only optical images and downscaling by fusing microwave and optical remote sensing data. The results showed that the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Auxiliary Dataset** & **Target** & **Time Span** & **Time R** & **Space R** & **RMSE (cm\({}^{3}\)/cm\({}^{3}\))** \\ \hline \hline \multirow{2}{*}{SMRFM [10]} & NDVI/LST/DEM & 25 km–1 km & 2010.8–2010.9 & \multirow{2}{*}{0.24–0.72} & \multirow{2}{*}{0.062–0.112} \\  & & & 2011.6–2011.9 & & & \\ \hline \multirow{2}{*}{RF/KKNN [32]} & Latitude/Longitude/Elevation/Aspect/Slope & \multirow{2}{*}{25 km–1 km} & \multirow{2}{*}{2010.1–2010.12} & \multirow{2}{*}{–0.04–0.46} & \multirow{2}{*}{0.06–0.132} \\  & Elevation/Aspect/Slope & & & & \\ \hline \multirow{2}{*}{RF [31]} & LAI/ALB/NDVI/DEM/EVI/NDWI & \multirow{2}{*}{36 km–1 km} & \multirow{2}{*}{2015–2016} & \multirow{2}{*}{0.6} & \multirow{2}{*}{0.038–0.073} \\  & DEM/EVI/NDWI & & & & \\ \hline \multirow{2}{*}{Dis-PATCH [12]} & LST/NDVI/Elevation & \multirow{2}{*}{25 km–1 km} & \multirow{2}{*}{2010.6–2011.5} & \multirow{2}{*}{–0.02–0.45} \\  & & & & & \\ \hline \multirow{2}{*}{RF/BRTs [19]} & ALB/LST/NDVI/Elevation & \multirow{2}{*}{25 km–1 km} & \multirow{2}{*}{2007.5–2007.9} & \multirow{2}{*}{0.12–0.83} \\  & & & 2010.11–2011.3 & & & \\ \hline \multirow{2}{*}{DENSE [38]} & LST/LSR/Elevation & \multirow{2}{*}{36 km–1 km} & \multirow{2}{*}{2015-2017} & \multirow{2}{*}{0.36–0.84} & \multirow{2}{*}{0.10–0.57} \\  & & & & & \\ \hline \multirow{2}{*}{WDL [1]} & TB-h/TBw/DEM/PRE/Soil properties/LC & \multirow{2}{*}{36 km–1 km} & \multirow{2}{*}{2015.4–2017.11} & \multirow{2}{*}{0.76–0.83} & \multirow{2}{*}{0.10–0.57} \\  & & & & & \\ \hline \multirow{2}{*}{Linear statics [48]} & Sentinel-1 & \multirow{2}{*}{9 km–1 km} & \multirow{2}{*}{2017.1–2015.5} & \multirow{2}{*}{0.7} & \multirow{2}{*}{0.054–0.114} \\  & NDVI/LST/DEM/LAB & \multirow{2}{*}{36 km–1 km} & \multirow{2}{*}{2016.1–2016.12} & \multirow{2}{*}{0.6–0.8} & \multirow{2}{*}{0.091–0.109} \\  & & & & & \\ \hline \multirow{2}{*}{RF [18]} & LST/NDVI/EVI/LAB/Precipitation/Solit texture & \multirow{2}{*}{36 km–1 km} & \multirow{2}{*}{2017–2018} & \multirow{2}{*}{0.52} \\ \hline \multirow{2}{*}{Bayesian [51]} & LST/LSR/ATI & \multirow{2}{*}{25 km–1 km} & \multirow{2}{*}{2013.8–2013.10} & \multirow{2}{*}{0.88} & \multirow{2}{*}{0.019–0.046} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Published soil moisture downscaling studies. fusion method can significantly improve the spatial resolution of soil moisture. The specific conclusions are as follows:  
(1) The spatial distribution of soil moisture is consistent before and after downscaling, but the downscaled soil moisture is closer to the actual surface measurement. This proves the feasibility of the adopted method and provides a basis for studying soil moisture-related issues in the region. (2) The verification results showed that high-quality SAR backscattering coefficient data can effectively supplement the shortcomings of microwave data in spatial resolution. Compared with single optical data, the fusion model can better capture the spatial changes of soil moisture, which significantly improved the downscaling effect of soil moisture. (3) The research results provide a new perspective for soil moisture monitoring and have broad application prospects. Especially in the fields of agricultural management, drought monitoring, and water resources management, the downscaling method of this study can provide more accurate information support for decision-making. Future research should consider the impact of different surface types and climate conditions on soil moisture downscaling, improve the optimization strategy of the algorithm, enhance the generalization ability of the model, and improve the universality of the model. Conceptualization, Q.Y. ; Software, R.Z. ; Validation, R.J.; Writing--original draft, J.W.