## 2 Materials and Methods
### Data Source and Processing  
The data processing roadmap is shown in Figure 2. A summary of all data used in this study is listed in Table S1. The BESS model dataset ([[http://environment.snu.ac.kr/](http://environment.snu.ac.kr/)]([http://environment.snu.ac.kr/](http://environment.snu.ac.kr/)), accessed on 1 August 2021) provides time series of global GPP and ET data from 2001 to 2015 at the spatial resolution of 1 km and the temporal resolution of 8 days [44; 45]. The BESS model is a process-based biophysical model that uses seven MODIS atmosphere and land products, four other satellite datasets, four reanalysis datasets and three ancillary datasets. The BESS model incorporates atmosphere and canopy radiative transfers, canopy photosynthesis, evapotranspiration and energy balance processes [44; 45]. Some researchers evaluated the performance of the BESS model on a global scale using FLUXNET ground observations, and the results showed that the BESS model outperformed the MODIS products in general [45; 46]. The MODIS LAI products were delivered at 500 m spatial resolution and 8-day composite temporal resolution from 2000 to the present ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)), accessed on 1 August 2021). To be consistent with the BESS dataset resolutions, we resampled the LAI data to the 1 km spatial resolution and used the period spanning from 2001 to 2015. Evaluation studies have found that the MODIS LAI well agreed with the FLUXNET observations at the site scale [47]. The MODIS LAI product has been widely used in studying vegetation phenology, ecosystem productivity and global vegetation variation [48; 49; 50]. The ecosystem indicators (ET, GPP, LAI) were cumulated over the entire year. Daily meteorological data from 66 meteorological stations were acquired from the China Integrated Meteorological Information Sharing System ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 1 August 2021). This dataset undergoes strict quality control before data release. Land cover data were downloaded from the European Space Agency ([[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)), accessed on 1 August 2021). DEM data were obtained from  
Figure 1: Digital elevation model (DEM) (**a**) and the studied observation sites on top of land cover maps in Xinjiang (**b**). Note: The five observation sites are Wulanwusu, Fukang, Jinghe, Korla, and Aksu stations. Figure 2: Technical flow chart. the website of geospatial data cloud ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)), accessed on 1 August 2021). We used daily temperature and precipitation to calculate mean annual temperature (TEM), and total annual precipitation (PRE) for each year from 2001 to 2015. The studied temperature and precipitation data were interpolated with the spatial resolution of 1 km by the ANUSPLIN software, taking elevation (DEM) as a covariate. The ANUSPLIN has excellent performance in interpolating climate data in Xinjiang [51]. In addition, we collected model validation data, including ET and GPP, from previous publications [52; 53; 54]. Monthly GPP data in 2009, 2010, 2012, and 2013 were measured by an eddy covariance system in a typical cropland in Wuhanusus Town (44\({}^{\circ}\)17\({}^{\prime}\)N, 85\({}^{\circ}\)49\({}^{\prime}\)E) [52]. The 16-day composited ET and GPP from 2007 to 2009 were collected using an eddy covariance system in a shrubland in Fukang County (44\({}^{\circ}\)17\({}^{\prime}\)30\({}^{\prime}\)N, 87\({}^{\circ}\)56\({}^{\prime}\)16\({}^{\prime}\)E) [53]. The 16-day composited ET data in 2013 were measured by a micro lysimeter in an alpine meadow in Aksu (41\({}^{\circ}\)42\({}^{\prime}\)N, 80\({}^{\circ}\)10\({}^{\prime}\)E) [54].