## 2 Materials and Methods
### Data
#### 2.2.2 Remote Sensing Product  
The GLASS (Global Land Surface Satellite) GPP product was provided by the Beijing Normal University Center for Global Change Data Processing and Analysis ([[http://www.bru-datacenter.com/](http://www.bru-datacenter.com/)]([http://www.bru-datacenter.com/](http://www.bru-datacenter.com/))) (accessed on 5 April 2021). Its time resolution is 8 days, and the spatial resolution is 0.05\({}^{\circ}\). This product has been quality-controlled and had its accuracy verified, and it has the advantages of high accuracy, long time series, and spatial integrity [28; 29].  
The FLUXCOM GPP product (www.fluxcom.org) (accessed on 12 April 2021) provides carbon fluxes at a high spatial resolution of 0.05\({}^{\circ}\), with a temporal resolution of 8 days, and it is available for the Moderate Resolution Image Spectroradiometer (MODIS, 2000-present). FLUXCOM combines carbon and energy fluxes and meteorological measurements from 224 global FLUXNET sites, using machine learning techniques to scale up these fluxes to a global extent [30; 31]. [PERSON] et al. [32] evaluated FLUXCOM GPP products against observations and found that the FLUXCOM dataset adequately estimated global carbon fluxes. Long-term time-series GLASS GPP products were used to evaluate the climatology and trend of the GPP simulated using CLM5.0-BGCDV.  
The improvements in GPP products, including near-infrared reflectance (NIRv) and solar-induced chlorophyll fluorescence (SIF), provide a method to estimate global GPP [33]. Limited by its short duration, satellite SIF can hardly be used to monitor long-term GPP [34]. In this study, the global long-term (1982-2018) GPP dataset [35] generated by the proxy of GPP used satellite-based near-infrared reflectance to study the response of vegetation to regional climate change. Previous studies showed that the long-term datasets derived from NIRv better capture the seasonal and inter-annual variations in terrestrial GPP at the global scale [35].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Lat (\({}^{\circ}\)N)** & **Lon (\({}^{\circ}\)E)** & **Elevation (m)** & **V Vegetation Type** & **Years** \\ \hline Maqu & 33.9 & 102.17 & 3471 & Alpine meadow & 2010–2016 \\ Dangx & 30.85 & 91.08 & 4250 & Alpine meadow & 2007–2010 \\ Haibei & 37.62 & 101.3 & 3148 & Alpine meadow & 2003–2007 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information on the observation sites on the TP used in this study.  
In this study, a GPP dataset derived from the Moderate Resolution Imaging Spectro-radiometer (MODIS) instruments on the National Aeronautics and Space Administration (NASA) Terra and Aqua satellites was used ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/))) (accessed on 15 March 2022). The MODIS GPP product provides global daily estimates of GPP at a 0.05\({}^{\circ}\) resolution for the period 2000-2003 [36]. MCD43C4v006 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products were used as inputs to neural networks that were used to upscale the GPP estimated from the FLUXNET 2015 eddy covariance tower sites.  
The fractions of land cover types used in this study were sourced from the Global Vegetation Continuous Fields product (MOD44B, hereafter called MEaSURES), based on observations from the MODIS [37]. This product comprises three different land cover types: (1) bare soil, (2) trees, and (3) non-trees. It can be downloaded from NASA's Earth data portal ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/))) (accessed on 10 May 2020). Specific descriptions of the remote sensing products used in this study are presented in Table 2.