## 2 Materials and Methods
### Data Sources  
In this study, the NDVI, EVI, LAI, GPP, and NPP were selected as parameters to characterize vegetation growth dynamics. These variables were obtained from MODIS datasets as outlined in Table 1. The NDVI was computed based on the reflectance values between red (620-670 nm) and near-infrared (841-876 nm) bands, while the EVI was derived from the reflectance values across red, blue (459-479 nm), and near-infrared bands. Annual datasets for NDVI, EVI, and LAI were constructed by calculating the mean values for each year. The MOD17A3 HGF dataset was utilized to represent vegetation productivity, encompassing both GPP and NPP, with a temporal resolution of 1 year, facilitating direct utilization in the study.  
\[NDVI=\frac{\rho_{NIR}-\rho_{RED}}{\rho_{NIR}+\rho_{RED}} \tag{1}\]  
\[EVI=\frac{2.5\times(\rho_{NIR}-\rho_{RED})}{\rho_{NIR}+6\times\rho_{RED}-7.5 \times\rho_{BLUE}+1} \tag{2}\]  
where \(\rho_{NIR}\), \(\rho_{RED}\), and \(\rho_{BLUE}\) are the reflectance values of the near-infrared, red, and blue bands after atmospheric correction, respectively.  
The data utilized in this study include MCD12Q1/MOD15A2H/MOD17A3 HGF covering the period from 2001 to 2020, with the MOD/MYD13A1 data being incomplete due to the commencement of MYD13A1 data in July 2002. Consequently, only the annual mean values of MOD13A1 were considered for the NDVI and EVI in 2001 and 2002, while the annual mean values of both MOD13A1 and MYD13A1 were used for the years 2003 to 2020, respectively.  
The MCD12Q1 dataset, updated in 2001, was employed for land use type (LUT) differentiation, with the IGBP system comprising seventeen types, including natural vegetation and human-altered and non-vegetation types, being selected for classification. This study integrated the IGBP classification system into six types, as illustrated in Figure 2, based on the data characteristics and previous research by [PERSON]'s team [57; 58; 59].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Data** & **EI** & **Spatial Resolution** & **Time Resolution** \\ \hline MCD12Q1 & LULC & 500 m & 1 Year \\ MOD/MYD13A1 & EVI/NDVI & 500 m & 16 Days \\ MOD15A2H & LAI & 500 m & 8 Days \\ MOD17A3 HGF & GPP/NPP & 500 m & 1 Year \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data source in this study.  
Figure 2: The original and integrated land cover/use types.  
In this study, we utilized the 1 km national spatial resolution dataset on air temperature and precipitation developed by [PERSON] et al. [60]. The dataset covers the period from 2000 to 2012 and was derived from the \"Daily value dataset of China's surface climatic data\" compiled by the National Meteorological Information Center of China. The data preprocessing procedure involved various steps, such as data reading, merging, verification, statistical analysis, and the creation of a spatial interpolation batch code. Subsequently, the annual temperature and precipitation dataset with a spatial resolution of 1 km was produced using ANUSPLINE interpolation software (version 4.3) [61; 62]. The dataset is accessible for download at [[http://www.sciencedb.cn/dataSet/handle/319](http://www.sciencedb.cn/dataSet/handle/319)]([http://www.sciencedb.cn/dataSet/handle/319](http://www.sciencedb.cn/dataSet/handle/319)) (accessed on 20 December 2023), and for the purposes of this study, it was updated to include data up to 2018. To align with the spatial resolution of the land use and ecological parameters considered in this research, the dataset was resampled by the bilinear method to a resolution of 500 m.