## 2 Materials and Methods
### Satellite Remote Sensing Datasets  
The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) dataset was available at yearly intervals and a 500 m spatial resolution. This product was derived from a time series of the 2-band enhanced vegetation index (EVI2), which was calculated from the MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Collection 6 of the MCD12Q2 dataset, which covered the period from 2001 to 2018 in the Third Pole, was obtained from Google Earth Engine ([[https://earthegine.google.com/](https://earthegine.google.com/)]([https://earthegine.google.com/](https://earthegine.google.com/)), accessed on 27 April 2022). The Greenup and Greendown of the MCD12Q2 SDS layers were selected to extract the start of the season (SOS) and end of the season (EOS).  
The MODIS NDVI dataset (MOD13A3) was available at monthly intervals and a 1 kilometer (km) spatial resolution. The products were computed from atmospheric corrected surface reflectance, which accounted for the removal of molecular scattering, ozone absorption, and aerosols. Collection 6 of the MOD13A3 from 2001 to 2018 in the Third Pole was accessible through the USGS website ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)), accessed on 19 June 2022). Some images were preprocessed to remove null values (\(-\)3000) and display only the values within the range of \(-\)2000 to 10000, as stated in the user guide. The original images were resampled to align with the spatial resolution of the meteorological forcing datasets (0.1\({}^{\circ}\)). Monthly images were created by calculating the mean value of each pixel. To eliminate the effects of cloud cover and snow contamination on the NDVI in high-elevation areas, the pixels with NDVI \(<\) 0 were replaced by 0.  
The global OCO-2-based SIF product (GOSIF) was available at an 8-day resolution (as well as at monthly and annual resolutions) and 0.05-degree spatial resolution during the period of 2000-2021. This GOSIF product was derived from discrete OCO-2 SIF soundings, remote sensing data from the MODIS, and meteorological reanalysis using a data-driven method [31]. The product also had advanced spatial and temporal resolutions and much longer records compared with the coarse-resolution gridded SIF data that were directly aggregated from OCO-2 SIF soundings [31]. The spatial patterns and seasonal cycles of the GOSIF showed consistency with the coarse SIF data, and these strongly correlated with independent GPP data from 91 tower sites [31]. A detailed description of the GOSIF data (production, verification, and application) can be found in the GOSIF user guide. The monthly images were preprocessed to remove null values (32,767 and 32,766) and were resampled to spatially match the spatial resolution of the meteorological forcing datasets (0.1\({}^{\circ}\) spatial resolution). Pixels with GOSIF \(<\) 0 were replaced with 0. The monthly GOSIF data were used in this study to examine the spatial and temporal changes and trends of alpine vegetation in the Third Pole from 2001 to 2018. The relationship between the GOSIF data and environmental factors (frost days, air temperature, precipitation, and radiation) was discussed at both the growing season and seasonal scales.  
The digital elevation model (DEM) data for the Third Pole was obtained from the Shuttle Radar Topography Mission ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 19 April 2023), with a spatial resolution of 30 m. The boundary of the Third Pole was acquired from the Global Change Research Data Publishing & Repository ([[https://www.geodoi.ac.cn/](https://www.geodoi.ac.cn/)]([https://www.geodoi.ac.cn/](https://www.geodoi.ac.cn/)), accessed on 19 April 2023) [40].