## Materials and Methods
### Data Sources and Processing  
Normalized difference vegetation index (NDVI) reflects vegetation growth trend and is widely used to describe the physiological condition of vegetation [17]. Here, NDVI data were provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) products ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/))) at a spatial resolution of 500 m \(\times\) 500 m and temporal resolution of 16 days. The time-series data covers 2000-2019. We selected MOD13A1 NDVI data of four imagery data sets, including h25v05, h26v04, h26v05, and h27v05. NASA's MRT (MODIS Reprojection Tools) software was used for data processing, including image mosaic, projection, format transformation, and resampling. Then, vector boundaries of the upper and middle reaches of the YLB were used to extract the study area in ArcMap 10.2.2. The synthetic NDVI data may have outliers or missing values. Therefore, the MODIS NDVI datasets were smoothed and denoised by the Savitzky-Golay (S-G) filter [30; 31]. The basic idea is first to filter the data after reprojection, and then the pixels with good quality remain unchanged, while those with poor quality are filtered to form a new NDVI sequence. Considering that NDVI is easily saturated in dense vegetation areas, we also introduced two  
Figure 1: Location and altitude (**a**), stations and climate classification (BWk: cold desert; BSk: cold steppe climate; Cwa: dry-winter humid subtropical climate; Dwa: hot-summer continental climate; Dwb: warm-summer continental climate; Dwc: boreal climate; ET: tundra climate; EF: ice-cap climate) (**b**), main tributaries (**c**), and land cover types in 2019 (**d**) of the upper and middle reaches of the Yellow River Basin. data products, leaf area index (LAI) and gross primary productivity (GPP) to independently verify the robustness of the NDVI results. LAI and GPP data (MOD15A2H) are also derived from the MODIS products ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/))). The LAI and GPP dataset are available at a spatial resolution of 500 m \(\times\) 500m and 8-day temporal resolution. All pretreatments were the same as for NDVI. Temperature affects many plant processes, such as photosynthesis, respiration, and transpiration [26]. In arid and semi-arid areas, vegetation is very sensitive to precipitation [21]. Sunshine is the source of energy for plant growth [32]. Therefore, precipitation, temperature and sunshine duration were selected as meteorological indicators in this study. Monthly mean temperature and monthly total precipitation with a spatial resolution of 0.00833\({}^{\circ}\) were obtained from the Institute of Tibetan Plateau Research, Chinese Academy of Sciences ([[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/))). Daily sunshine duration (meteorological station data) was acquired from the Chinese National Bureau ([[https://data.cma.cn/](https://data.cma.cn/)]([https://data.cma.cn/](https://data.cma.cn/))) (Figure 1b). The period for time-series meteorological data is 2000-2019. To maintain the same spatial resolution, vegetation indexes and shine duration were resampled or interpolated into a grid cell with a spatial resolution of 0.00833\({}^{\circ}\). We obtained the land cover data (MCD12Q1) from the United States Geological Survey (USGS) platform ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/))). The MCD12Q1 supplies global maps of land cover at annual time steps and 500 m spatial resolution since 2001 and was classified according to the International Geosphere-Biosphere Program (IGBP) land use classification scheme.