# Vegetation Health in China Is Severely Compromised by Drought, Wet and Heat Stress Events
## 2 Materials and Methods
### Data  
The data used in this study include climate data (temperature, precipitation, PDSI, PM2.5, maximum temperature, etc.), soil attributes (TN, TP, SOM), socioeconomic data (GDP, tertiary industry value added, population density, nighttime lights), vegetation type data, and vegetation-related indices (GPP, NDVI, LAI, NPP, etc.). Meteorological data were used to construct the vegetation health assessment system and identify stress events, while soil attributes, socioeconomic data, and some vegetation indices were utilized to develop the vegetation health system. The LAI, NPP, and SIF data were employed to validate the model's performance and accuracy (Table 1). We determined the performance of the vegetation health system using goodness of fit (\(R^{2}\)) and Pearson correlation coefficients. The vegetation type data at a 1:1,000,000 scale was masked to retain only vegetation areas.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Data Type** & **Spatial Resolution** & **Temporal Resolution** & **Source** \\ \hline Temperature & & & [[http://www.geodata.cn/data/datadetails.html?](http://www.geodata.cn/data/datadetails.html?)]([http://www.geodata.cn/data/datadetails.html?](http://www.geodata.cn/data/datadetails.html?)) \\  & & & dataiguid=67669514169502&docld=4 (accessed on 12 March 2024) \\  & 0.008333\({}^{\circ}\) & Yearly & [[https://www.geodata.cn/data/datadetails.html?](https://www.geodata.cn/data/datadetails.html?)]([https://www.geodata.cn/data/datadetails.html?](https://www.geodata.cn/data/datadetails.html?)) \\ Precipitation & & & 12 March 2024) \\ \hline
2 m temperature & 0.1\({}^{\circ}\) & Monthly & [[https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=overview](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=overview)]([https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=overview](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=overview)) (accessed on 12 March 2024) \\ \hline GPP & 500 m & 8 day & [[https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv061/]([https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv061/) (accessed on 6 May 2023) \\ \hline Land Surface Temperature, SR, RH & 0.1\({}^{\circ}\) & Monthly & [[https://cds.climate.copernicus.eu/#](https://cds.climate.copernicus.eu/#)]([https://cds.climate.copernicus.eu/#](https://cds.climate.copernicus.eu/#))!/home (accessed on 15 March 2024) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of data.  
Figure 1: Spatial distribution of (**a**) elevation, (**b**) subregions of China and (**c**) vegetation types.  
To ensure the spatial resolution consistency of vegetation health data across China, bilinear interpolation was applied to resample all data to a resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\). This technique estimates the value for a specific location by utilizing the four nearest data points surrounding it. It applies the principle of two-dimensional linear interpolation, deriving an interpolated value to approximate the value at the designated point [29].