## 3 Materials and Methods  
### Datasets  
In this study, Global land surface satellite (GLASS) LAI, FVC and GPP products for the period of 1982-2013 were used to analyze vegetation dynamics. These GLASS products were released using the Hierarchical Data Format (HDF), accessible to the public for free at [[http://glass-product.bnu.edu.cn](http://glass-product.bnu.edu.cn)]([http://glass-product.bnu.edu.cn](http://glass-product.bnu.edu.cn)). The detailed information of land surface parameter products, representative of the vegetation dynamics used in this study are presented in Table 1. The monthly LAI and FVC values were computed using the maximum value composite (MVC) technique [52], which is widely used in compositing NDVI and other vegetation indexes in order to minimize the interference of atmospheric effects, the scan angle, cloud contamination, and solar zenith angle [29; 53; 54; 55; 56; 57].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Product** & **Sensor** & **Spatial Resolution** & **Temporal Resolution** & **Data processing** & **Reference** \\ \hline LAI & GLASS & AVHRR & about 5 km & 8-day & \begin{tabular}{c} (1) Use MVC technique to \\ produce monthly data \\ based on 8-day products \\ (2) Average monthly data \\ to obtain annual data \\ \end{tabular} & [19; 58] \\ \hline FVC & GLASS & AVHRR & about 5 km & 8-day & \
\begin{tabular}{c} (1) Use MVC technique to \\ produce monthly data \\ based on 8-day products \\ based on 8-day products \\ (2) Average monthly data \\ to obtain annual data \\ \end{tabular} & [59] \\ \hline GPP & GLASS & AVHRR & about 5 km & 8-day &
\begin{tabular}{c} Summing 8-day products \\ to obtain annual data \\ \end{tabular} & [60] \\ \hline \hline \end{tabular}  
* NOTE: LAI: Leaf area index; FVC: Fractional vegetation cover; GPP: Gross primary productivity; GLASS: Global land surface satellite; AVHRR: Advanced Very High Resolution Radiometer; MVC: maximum value composite.  
\end{table}
Table 1: Land surface parameter products used in this study as representative of vegetation dynamics.  
Figure 1: Geographic map showing the location of the Amur River Basin in Eurasia with the topographic information attached and the locations of 193 meteorological stations used for this study.  
According to characteristics of climate changes during different periods in the ARB that have described in previous studies [61], in addition to the land use/cover transformations reported in the literature [36; 40; 62], the entire study period was divided into four periods to analyze the spatial-temporal relationships between environmental factors and vegetation dynamics: 1982-1990 (Period 1), 1991-1999 (Period 2), 2000-2006 (Period 3), and 2007-2013 (Period 4). The detailed information of datasets used in this study as representative of environmental factors are presented in Table 2. To investigate the driving factors of climate on vegetation changes, temperature and precipitation data observed from 1982 to 2013 in 193 meteorological stations within the basin were used. The climate data were interpolated at a spatial resolution of 8.8 \(\times\) 8.8 km by using thin plate smoothing splines and Anusplin software, and the LAI, GPP, and FVC datasets in the same duration were resampled to the same spatial resolution. The combined datasets of Chinese land use maps with global land use maps from the closest dates in the four historical periods were used as inputs for the GTWR model to investigate the anthropogenic driving factors responsible for regional vegetation changes (urbanization and wetland reclamation, etc.). In addition, the hydrological variables simulated by the SWAT model, as presented in our previous study [61], were adopted to analyze the relationship between hydrological variables and vegetation dynamics.