## 3 Study Area and Data
### Other Geographic Auxiliary Data  
To ensure the comprehensiveness and reliability of the soil moisture inversion model, we used the surface environmental factors around the measurement stations as input data sets. These data are (1) Latitude and longitude information, calculated by ArcGIS based on World Geodetic System-1984 (WGS-84) Coordinate System. (2) Land cover type, extracted from the International Geosphere-Biosphere Program (IGBP) land cover map based on Moderate Resolution Imaging Spectroradiometer (MODIS) to obtain land cover class data [54]. (3) normalized difference vegetation index, based on the MODIS water surface reflectance daily global 500 m dataset [55], was calculated from the near-infrared (NIR) and infrared (RED) bands of the reflectance data. The formula is NDVI = (NIR \(-\) RED)/(NIR \(+\) RED). We used a sliding window of more than 16 days on average, centered on calculating the NDVI for a particular day (eight days ahead and seven days past), obtaining the NDVI value with slight fluctuation in short periods. (4) Topographic factors, which are usually used to assess the influence of coarse surface over topography on soil moisture, are accessed through the Google Earth Engine for the dataset [56]. Topographic data (elevation, slope, slope direction, and shading) from the NASA Shuttle Rader Topography Mission Digital Elevation 30m dataset were extracted with the help of the extremely high computing power of the Google Cloud Platform. (5) Rainfall and air temperature are critical meteorological parameters for the surface environment and climate change and also play an essential role in vegetation growth and have a more substantial influence on soil moisture [57]. We obtained the US region's daily average air temperature and rainfall through the Phase Rotated Intense Slow Moonbeam (PRISM) project [58]. (7) NASA-USDA global soil moisture data generated based on SMOS data have a low spatial resolution and include surface and subsurface soil moisture data. In this paper, we select the surface soil moisture of NASA-USDA global soil moisture data as the preliminary comparison and validation data for the point-surface fusion results. All the data mentioned above are available for download through the Google Earth Engine, and the specific products used in this study are listed in Table 2 below.  
The above table shows that the maximum temporal resolution for all surface environmental parameters is one day. Meanwhile, the Google Earth Engine sets the spatial image resolution to 500 m when outputting images with specified spatial resolution according to the image pyramid strategy. Therefore, this study's final multi-data fusion of soil moisture products has a spatial and temporal resolution of 500 m per day.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Environmental** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **Spatio-** \\ **Temporal** \\ **Resolution** \\ \end{tabular} & **Project** & **Time** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Auxiliary \\ geographic \\ environment \\ data \\ \end{tabular} } & NDVI & 1 day/500 m & MOD09 GA & \begin{tabular}{c} 1 January 2014–1 \\ March 2014 \\ \end{tabular} \\ \cline{2-5}  & Land cover type & 1 day/500 m & MCD12Q1 & 2014 \\ \cline{2-5}  & Rainfall, air & \multirow{2}{*}{1 day/2.5\({}^{\prime}\)} & \multirow{2}{*}{PRISM} & \
\begin{tabular}{c} 1 January 2014–1 \\ March 2014 \\ \end{tabular} \\ \cline{2-5}  & Elevation, slope, & & & \\ slope direction, & & & & \\ shading & & & & \\ \hline \multirow{2}{*}{Validation data} & NASA-USDA & \multirow{2}{*}{3 days/0.25\({}^{\circ}\)} & \multirow{2}{*}{NASA GSFC} &
\begin{tabular}{c} 1 January 2014–1 \\ March 2014 \\ \end{tabular} \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Auxiliary data used in the study.