# Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
## 2 Materials and Methods
### Overview of the Study Area and Data Sources
#### 2.1.3 Data Pre-Processing  
Based on the University of Maryland (UMD) Land Cover Classification Scheme (LCCS), the Biogeochemical Cycle (BGC) classification scheme of the Food and Agriculture Organization of the United Nations (UN FAO), and related research results [34; 35], we reclassified the land cover data of the study area and extracted more than 30 land cover types (Table 2). Owing to the rich diversity of vegetation cover types in tropical regions, we preserved the original classification of non-vegetated areas. For vegetated zones, we refined our classification method by grouping these areas according to species composition similarity. This approach enhances the accuracy of reflecting the ecological characteristics and interrelations among the diverse vegetation types found in these regions. Subsequently, we identified 14 primary land cover types, including cropland, mosaic cropland, evergreen broadleaf forest, broadleaved deciduous forest, deciduous coniferous forest, shrubland, evergreen needle-leaved forest, urban, grassland, wetland, bare areas, water, snow and ice, and tree mixed (Figure 2). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Data Sources** & **Web Address** & **Spatial Resolution** & **Time Resolution** \\ \hline Total Precipitation & ERA5 & [[https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)]([https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)) (accessed on & 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) \\
2 m temperature & ERA5 & [[https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)]([https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)) & 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) \\ Solar Shortwave Radiation & ERA5 & [[https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)]([https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)) & 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) \\ Downwards (SSRD) & ERA5 & [[https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)]([https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)) & 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) \\ Surface Pressure (SP) & ERA5 & [[https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)]([https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)) & 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) \\ Relative Humidity (RH) & ERA5 & [[https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)]([https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)) & 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) \\ Leaf Area Index (LAI) & GLASS & [[http://www.glass.umd.edu/Download.html](http://www.glass.umd.edu/Download.html)]([http://www.glass.umd.edu/Download.html](http://www.glass.umd.edu/Download.html)) & 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) \\ Atmospheric CO\({}_{2}\) & GCdataPR & [[https://www.geodo.ac.cn/](https://www.geodo.ac.cn/)]([https://www.geodo.ac.cn/](https://www.geodo.ac.cn/)) (accessed on 10 & 2\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\) \\ Concentration (CB) & November 2023) & 2\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\) \\ Land Cover & ERA5 & [[https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)]([https://cds.climate.opernicus.eu](https://cds.climate.opernicus.eu)) & 300 m \(\times\) 300 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research data. Figure 2: Comparison before and after reclassification of land cover types, 2001. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
In calculating the GPP, all the data we used were monthly data with a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\). To harmonize their spatial resolution, we resampled the relative humidity and carbon dioxide concentration data to adjust their spatial resolution to 0.1\({}^{\circ}\).