## 2 Materials and Methods
### The 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\) Fine-RES Products  
Global Land Surface Satellite (GLASS, [[http://www.geodata.cn/thematicView/GLASS.html](http://www.geodata.cn/thematicView/GLASS.html)]([http://www.geodata.cn/thematicView/GLASS.html](http://www.geodata.cn/thematicView/GLASS.html)), accessed on 21 June 2022) datasets were used in this work. GLASS datasets include a series of high-precision, spatially continuous, long-term global products, i.e., leaf area index (LAI), surface broadband albedo (albedo), fractional vegetation cover (FVC), gross primary production (GPP), evapotranspiration (ET), etc. Here, the 1 km \(\times\) 1 km GLASS FVC [57], 500 m \(\times\) 500 m LAI [58], and albedo [59] products were adopted as independent variables to construct the fitting models. They were mosaicked and reprojected to cover the QTP using the MODIS Reprojection Tool. Next, nearest-neighbor interpolation was utilized to resample them to a spatial resolution of 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\). All pixels classified as water or ice/snow were removed. Land-surface temperature (LST) is considered to be a dominant energy parameter describing surface water and heat conditions [29; 60]. The 1 km Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST-Tibetan Plateau (TRIMS LST-TP, [[https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)]([https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)), accessed on 21 June 2022) [61; 62] was selected to overcome the challenges of thermal infrared remote sensing detection as a result of temporal/spatial gaps and misdetection due to cloud/topography causes [63; 64]. TRIMS LST leverages both high spatial resolution from MODIS LST and all-weather capability from AMSR-E/ AMSR2 passive microwave brightness temperature. [PERSON] et al. demonstrated that LST is more sensitive to SM during daytime than nighttime, and has a stronger correlation with and sensitivity to SM than other factors [65]. [PERSON] et al. also concluded that downscaled SM results based on MODIS daytime LST are superior to results based on MODIS nighttime LST. As a result, the daytime TRIMS LST-TP product was applied here [66]. The above products, collected for training OWCMs and GMs during the unfrozen season from April to September 2016, were resampled and reprojected to fit the original RFSM SM product (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) and target (0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\)) scales. Two additional factors derived from them--the temperature vegetation dryness index (TVDI) and soil evaporation efficiency (SEE)--were also adopted as independent variables for the fitting procedure. TVDI is a compound temperature-vegetation parameter derived from the soil moisture dry-wet edge contour in the two-dimensional feature space of vegetation index and LST [67; 68; 69]. SEE is a parameter obtained from the improved calculation method for soil moisture evaporation efficiency [29; 70]. It is worth noting that the GLASS LAI from April 2014 to September 2016 was collected for in situ heterogeneity quantification, as shown in Section 3.2.1. Given the known topographical influence on SM, topography complexity index (Tci) is a composite exponent computed from a digital elevation model (DEM). Here, DEM information, as shown in Figure 1a, was acquired from the USGS global 30-arc second elevation dataset (GTOPO-30) with a spatial resolution of 30\({}^{\prime\prime}\), developed by the United States Geological Survey (USGS, [[https://lta.cr.usgs.gov/](https://lta.cr.usgs.gov/)]([https://lta.cr.usgs.gov/](https://lta.cr.usgs.gov/)), accessed on 21 June 2022). Tci was computed based on the following equation [56]:  
\[Tci=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(dem_{i}-\overline{dem}\right)^{2}}, \ \overline{dem}=\frac{\left(dem_{1}+\ldots+dem_{n}\right)}{n} \tag{1}\]  
where \(dem_{i}\), \(\overline{dem}\), and \(n\) are the \(i\)th, average value, and sample size of the 30\({}^{\prime\prime}\) DEMs within a 0.25\({}^{\circ}\) and 0.01\({}^{\circ}\) pixel, respectively.