## 2 Research Region and Data
### Material and Processing  
(1) Remote-sensing data  
For the research, the remote sensing data we required included vegetation-type data and NDVI data. Vegetation-type data images with a spatial resolution of 500 m (Figure 2) were retrieved from [[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)) (accessed on 10 August 2022) and were maintained by the NASA EOSDIS Land Processes Distributed Activity Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center ([[https://doi.org/10.5067/MODIS/MCD12Q1.006](https://doi.org/10.5067/MODIS/MCD12Q1.006)]([https://doi.org/10.5067/MODIS/MCD12Q1.006](https://doi.org/10.5067/MODIS/MCD12Q1.006)), accessed on 10 August 2022). The vegetation-type data were then reclassified into thirteen categories according to the needs of the study (Table 1). The spatial distribution dataset of China's monthly 1 km vegetation index (NDVI) was based on the ten-day 1 km vegetation index data of SPOT/VEGFATION PROBA-V 1 km PRODUCTS ([[http://www.vito-eodata.be](http://www.vito-eodata.be)]([http://www.vito-eodata.be](http://www.vito-eodata.be)), accessed on 6 June 2022) using the maximum value synthesis method. Figure 1: Map of the research region. (2) Meteorological data  
This research required the following meteorological data: monthly average temperature, monthly total precipitation, monthly total solar radiation, and potential evapotranspiration data. The temperature, precipitation, and evaporation data were obtained from National Earth System Science Data Center and National Science and Technology Infrastructure of China ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), accessed on 2 September 2022), with a spatial resolution of 1 km. The solar radiation data were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) ([[https://doi.org/10.24381/cds.68d2](https://doi.org/10.24381/cds.68d2) bb30]([https://doi.org/10.24381/cds.68d2](https://doi.org/10.24381/cds.68d2) bb30), accessed on 30 August 2022), with a spatial resolution of 1 km. (3) NPP validation data  
The NPP validation data required for the study included both station data and NPP product data. The data of eight carbon flux observation stations were downloaded from the China National Ecological Science Data Centre Resource Sharing Service Platform ([[http://www.chern.org.cn](http://www.chern.org.cn)]([http://www.chern.org.cn](http://www.chern.org.cn)), accessed on 30 October 2022); the specifics of the eight sites are listed in Table 2, with the shared flux data comprising the net ecosystem exchange (NEE) and ecosystem respiration (\(R_{e}\)). To validate and evaluate the reliability of the NPP simulation results, this study converted the flux tower observations from January to December 2010 to NPP (\(GPP=R_{e}-NEE\), \(NPP_{EC}=\alpha\times GPP\)) [30]. The NPP simulation results of the sites were extracted based on the latitude and longitude information, and a linear correlation analysis was used for NPP validation. The NPP product data used for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Class Names** & **Acronym** & \(\epsilon_{\text{max}}\) \\ \hline
1 & Deciduous Needleleaf Forest & DNF & 0.485 \\
2 & Evergreen Needleleaf Forest & ENF & 0.389 \\
3 & Evergreen Broadleaf Forest & EBF & 0.985 \\
4 & Deciduous Broadleaf Forest & DBF & 0.692 \\
5 & Shrublands & SHR & 0.429 \\
6 & Sparse forests & SPF & 0.475 \\
12 & Grasslands & GRA & 0.542 \\
13 & Urban and built-up & URB & 0.542 \\
15 & Water & WAT & 0.542 \\
16 & Wetlands & WET & 0.542 \\
17 & Snow and ice & SNO & 0.542 \\
20 & Deserts & DES & 0.542 \\
21 & Croplands & CRO & 0.542 \\ \hline \hline \end{tabular} \(\epsilon_{\text{max}}\) indicates maximum light use efficiency.  
\end{table} 
Table 1: Reclassified vegetation types. Figure 2: Spatial arrangement of vegetation types in China.