## 2 Materials and Methods
### Land Cover Data and Vegetated Grid Cells of Interest  
The multisource land cover products used in this study were MCD12C1 and ESACCI, representing qualitative and semiquantitative land cover products according to [PERSON] and [PERSON] [30]. The MCD12C1 land cover products describe the land cover properties derived from observations of Terra and Aqua MODIS data with a spatial resolution of 0.05\({}^{\circ}\) and the span of year, which can be downloaded at [[https://lpdaac.usgs.gov/products/mcd12c1v061/](https://lpdaac.usgs.gov/products/mcd12c1v061/)]([https://lpdaac.usgs.gov/products/mcd12c1v061/](https://lpdaac.usgs.gov/products/mcd12c1v061/)) (accessed on 29 August 2023). The product has been widely used to quantify global land cover with long time series. We used the IGBP global vegetation classification scheme with 17 land cover classes [41]. The ESACCI is a representative global semi-quantitative land cover product with a spatial resolution of 300 m, which can be obtained from [[http://maps.elie.ucl.ac.be/CCI/viewer/download/](http://maps.elie.ucl.ac.be/CCI/viewer/download/)]([http://maps.elie.ucl.ac.be/CCI/viewer/download/](http://maps.elie.ucl.ac.be/CCI/viewer/download/)) (accessed on 22 December 2019). The product provides a global map that divides the land surface into 37 categories, which are defined by the Land Cover Classification System (LCCS) of the Food and Agriculture Organization of the United Nations (FAO). The data show a high accuracy for the types of city, paddy field, and evergreen broad-leaved forest, and they have been widely used to analyze land cover transitions [30; 42]. Because this study aimed to quantify the proportion of plant functional types (PFTs), we summarized the ESACCI data with the 37 categories into grid cells at a 0.05\({}^{\circ}\) spatial resolution (consistent with MODIS) and converted them into PFTs [43] for the subsequent work.  
Given the natural vegetation in the study area, the proportion of each PFT was calculated through the PFT cross-walking table, as shown in Table 1. This study used a PFT-based ESACCI land cover product to explore LCC and estimate GPP. PFT can better determine the ecological process as well as detect and predict the vegetation response to a series of environmental changes [44; 45]. The ESACCI PFT-based land cover products were converted from qualitative pixel types to land cover percentages. When exploring the relationship between GPP and NIRv, the estimated GPP was weighted and summed according to the corresponding PFTs.  
In the study, we excluded bare ground through NDVI! 0.1, because it will not make any contribution to GPP [46]. There were croplands in the study area, including C3 and C4 croplands. Since this study only focused on the differences caused by land cover rather than specific species, only C3 cropland was taken as a proxy of cropland to calculate GPP.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**MODIS** & **ESA CCI (PFTs)** \\ \hline Deciduous broadleaf forest & Deciduous broadleaf tree \\ / & Evergreen broadleaf tree \\ Evergreen needleleaf forest & Evergreen needleleaf tree \\ Mixed forest & / \\ Closed shrub & Evergreen broadleaf shrub \\ Deciduous broadleaf shrub & Evergreen needleleaf shrub \\ Sparse shrub & Deciduous needleleaf shrub \\ Grass & Grass \\ \hline \hline \end{tabular}
\end{table}
Table 1: Land cover products used in this work.