## Appendix A Multifactorial Statistic Factor Selection (MFS) Process  
Anterior to operating the GMs and OWCMs in the SM fitting procedure, we utilized the MFS process to determine the appropriate fitting factors for the GMs and OWCMs. MFS can be regarded as an evaluation of the effectiveness of fitting factors. MFS evaluates four fitting factors, along with their combinations (i.e., the product of every two factors, the square and cube of every factor), within four typical land cover types over the QTP, i.e., grasslands, croplands, barren or sparsely vegetated, and mixed forest, according to the IGBP classification scheme (MODIS MCD12Q1 product). For each pixel of a coarse WT component (0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)), MFS is directly applied in aggregated pure pixels of the four typical land cover types, where there is only one typical land cover type of the four. For other mixed pixels, the land cover type is regarded as the type of which the IGBP original pixels (0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\)) account for the most significant proportion within it. For each pixel of a fine-resolution WT component (0.02\({}^{\circ}\)\(\times\) 0.02\({}^{\circ}\)), its land cover type is judged by the type accounting for the largest proportion within the 3 \(\times\) 3 neighborhood pixels (0.06\({}^{\circ}\)\(\times\) 0.06\({}^{\circ}\)). The MFS process involves two steps: (1) To identify the potentially relevant factors by comparing the consistency between the frequency distributions of each fitting factor and each WT-derived SM component in the probability density figure. (2) To determine the final fitting factors significantly correlated with each WT-derived SM component by testing partial correlation coefficients at 95% confidence intervals when fixing other fitting factors. Taking the most widely distributed land cover type of grassland as an example, the frequency distribution plots in the probability density of the four WT components (LL, HL, LH, and HH) are shown in Figure 11. For brevity, only the final selected factors are shown. Table 11 lists the corresponding fitting factors selected for the four WT components and their corresponding partial correlation coefficients over the four land cover types, i.e., grassland, cropland, barren or sparsely vegetated, and mixed forest. For GMs, the final selected factors are the same as the LL component, since LL is watched as the approximation image of the original. Figure 11: Frequency distributions in the probability density of the final selected fitting factors, with relatively good consistency with the frequency distribution of the original RFSM in the four WT components (LL, LH, HL, HH) over grassland. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Land Cover Types** & **WT Component** & **Selected Factors** &
\begin{tabular}{c} **Partial Correlation** \\ **Coefficient** * \\ \end{tabular} \\ \hline \multirow{4}{*}{Grasslands} & LL & LAI, LST & 0.6673,0.7247 \\ \cline{2-4}  & HH & LAI, LST, albedo \(\times\) FVC & 0.5487, 0.6924, 0.5765 \\ \cline{2-4}  & HL & LST, albedo \(\times\) FVC, LAI \(\times\) FVC & 0.4982, 0.4792, 0.6593 \\ \cline{2-4}  & LH & Albedo \(\times\) FVC, LST & 0.7873, 0.6169 \\ \hline \multirow{4}{*}{Croplands} & LL & FVC, LST & 0.8147, 0.7571 \\ \cline{2-4}  & LH & Albedo \(\times\) LAI, LAI \(\times\) FVC, albedo \(\times\) FVC & 0.6734, 0.5186, 0.4795 \\ \cline{2-4}  & HL & FVC \(\times\) LST & 0.6575 \\ \cline{2-4}  & HH & Albedo \(\times\) LAI, albedo \(\times\) LST & 0.5917, 0.4452 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the final selected factors by MFS for the four land cover types over the QTP. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta-analysis approach. _Water Resur. Res._**2016**, _52_, 3663-3684. [CrossRef]
* [PERSON] et al.