## 2 Materials and Methods
### Study Area and Data Processing
#### 2.2.2 Data Processing  
Due to the availability of time-series tower-based measurements, the study period of this work is from January to September 2020. As shown in Table 1, multiple datasets were used in this work. Besides tower-based measurements, all other data were prepared at 30 m resolution, including soil texture, LST (8-day), LAI (8-day), EVI (16-day), fraction of photosynthetically active radiation absorbed (FPAR, 8-day), land cover, and topographic features (i.e., elevation, aspect, slope, SVF, and watershed boundary). Soil texture maps at the 250 m resolution and MODIS LST at the 1 km resolution were resampled to 30 m resolution by assuming the soil type or LST of all the subpixels are the same as those of each coarse pixel. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Variable** & **Resolution** & **Reference** \\ \hline MCD15A2H 1  & LAI/FPAR & 500 m/8-day & [69] \\ MOD13Q1 Version 6 1  & NDVI/EVI & 250 m/16-day & [70] \\ MOD11A2 Version 6 1  & LST & 1 km/8-day & [71] \\ Sentinel-2 Level-1C 2  & Surface albedo & 10 m/10-day & [72] \\ Landsat-8 Level-1T 3  & Surface albedo & 30 m/16-day & [73] \\ SRTM DEM & Elevation & 30 m & [74] \\ Open Land Map & Soil properties & 250 m & [75] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of multiple datasets used in this study. Figure 1: Spatial characteristics of elevation (**b**), land cover (**c**), aspect (**d**), slope (**e**), and SVF (**f**) over Wanglang National Nature Reserve. The location of Wanglang National Nature Reserve in China is shown in sub-figure (**a**). * Tower-based data  
Tower-based measurements were adopted in this work, including half-hour air temperature, incoming solar radiation, precipitation, and vapor pressure deficit (VPD). Daily data were calculated from these half-hour measurements to drive the GPP models. All tower-based data were processed according to [PERSON], Lucas-Moffat [76], with detailed information available at the online service ([[https://www.bgc-jena.mpg.de/bgi/index.php/Services/](https://www.bgc-jena.mpg.de/bgi/index.php/Services/)]([https://www.bgc-jena.mpg.de/bgi/index.php/Services/](https://www.bgc-jena.mpg.de/bgi/index.php/Services/)), accessed on 23 December 2020). * Land cover map  
In this study, a high-resolution land cover map was obtained from multi-temporal Sentinel-2 surface albedo data. Four Sentinel-2 surface albedo data with difference dates were used to provide seasonal information [77], which is useful for LC classification. The Level-1C products were firstly pre-processed by radiometric calibration and atmospheric correction (i.e., Sen2 Cor). An object-oriented classification approach with a validation accuracy of ~89.68% [77], was then used to generate the 10-m land cover map from albedo information of the blue, green, red, and near-infrared bands. The main land cover types in the 10-m land cover map were evergreen needleleaf forest, deciduous broadleaf forest, mixed forest, shrubland, and grassland. To maintain consistency with the spatial resolution of other data, the 10-m land cover map was resampled to 30-m by assuming that the land cover type of each 30 m pixel was the dominant type (i.e., having the largest percentage of area) of all the 10-m subpixels. * Time-series LAI, FPAR, and EVI maps  
Figure 2: Seasonal variations in daily average air temperature (Tave, **a**), daily minimum air temperature (Tmin, **a**), daily maximum air temperature (Tmax, **a**), daily average soil temperature (Tsoil, **a**), daily average incoming solar radiation (**b**), daily average VPD (**c**) and daily precipitation (**c**) over Wanglang Reserve in 2020. To ensure the reliability of MODIS LAI, FPAR, and EVI time-series data, those values with corresponding quality control (QC) values less than 64 (i.e., LAI and FPAR) or quality assurance (QA) values less than 1 (i.e., EVI) were selected, and missing values gap-filled using the linear interpolation method.