## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Permanent** & **Frozen** & **Surface** & **Built-up** & **Dense** \\  & **Snow** & **Ground** & **Water** & **Structure** & **Vegetation** \\ \hline May 2016 & 93.22 & 84.60 & 79.23 & 81.17 & 78.24 \\ June 2016 & 92.16 & 86.71 & 76.26 & 81.55 & 76.72 \\ July 2016 & 94.06 & 91.23 & 82.65 & 80.86 & 75.48 \\ August 2016 & 91.32 & 90.38 & 79.89 & 81.26 & 79.33 \\ September 2016 & 92.27 & 93.12 & 77.63 & 80.26 & 80.17 \\ October 2016 & 89.52 & 88.21 & 79.46 & 83.50 & 77.60 \\ November 2016 & 91.46 & 85.81 & 80.01 & 83.80 & 80.33 \\ December 2016 & 92.29 & 84.23 & 78.57 & 79.59 & 79.27 \\ January 2017 & 93.51 & 83.41 & 79.06 & 78.88 & 78.40 \\ February 2017 & 93.96 & 84.81 & 81.51 & 82.90 & 74.64 \\ March 2017 & 93.79 & 85.19 & 76.66 & 78.79 & 77.56 \\ April 2017 & 93.69 & 83.22 & 76.03 & 80.39 & 77.66 \\ May 2017 & 90.13 & 85.01 & 76.89 & 82.75 & 80.60 \\ Avg & 92.41 & 86.61 & 78.76 & 81.21 & 78.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The proportion of pixels with no or insignificant presence of land cover in study area (%). Figure 3: Downscaling SMAP soil moisture image in the study area at 0.3-km resolution. (**a**) SMAP; (**b**) Proposed method; (**c**) Original method. Figure 4: Comparison diagram before and after downscaling process in the sampling area corresponding to A1 and A2, respectively. (**a**) Sample A1 using 9-km resolution SMAP product; (**b**) Sample A1 at 0.3-km resolution after downscaling using the Temperature Vegetation Drought Index (_TVDI_) as downscaling factor; (**c**) Sample A1 at 0.3-km resolution based on the original Soil Moisture Index (_SMI_) method; (**d**) Sample A2 using a 9-km resolution SMAP product; (**e**) Sample A2 at 0.3-km resolution after downscaling using the _TVDI_ as the downscaling factor; (**f**) Sample A2 at 0.3-km resolution based on the original _SMI_ method. Figure 5: Comparison diagram before and after downscaling process in the sampling area corresponding to B1 and B2, respectively. (**a**) Sample B1 using the 9-km resolution SMAP product; (**b**) Sample B1 at 0.3-km resolution after downscaling using the _TVDI_ as the downscaling factor; (**c**) Sample B1 at 0.3-km resolution based on the original _SMI_ method; (**d**) Sample B2 using 9-km resolution SMAP product; (**e**) Sample B2 at 0.3-km resolution after downscaling using _TVDI_ as the downscaling factor; (**f**) Sample B2 at 0.3-km resolution based on the original _SMI_ method. The second column in Figures 4 and 5 indicate that the improved method can reflect the difference in soil moisture. As can be seen from the comparison of each sampling area, the improved method can better reflect the spatial difference of soil moisture in areas with high vegetation coverage. Experimental results were analyzed by selecting 10 samples from each sampling area, which revealed that the average error of the sample point scale was higher than that of the SMAP grid scale. Overall, the estimation at a 0.3-km resolution was closer to the observed value, where the root means square error (RMSE) and absolute error (AE) of A1 were greater than that of B1 (Table 5). The error (RMSE and AEF) of area B1 was higher than A2, and the dispersion degree of data was lower than for A2. The error in the A2 region was slightly greater than in B2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sampling Area (no.