# Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region
## 3 Methods
### Indicator Selection and Data Processing  
As an important component of ecosystems, changes in vegetation directly affect the climate, soil and other factors, which can well be reflected as change in an ecosystem's quality [18]. Vegetation parameters estimated using remote sensing data have played a key role in environmental and ecological studies [19]. Therefore, it was reasonable to select vegetation parameters for evaluating the change in ecosystem quality. The leaf area index (LAI) is an essential parameter for characterizing the density of leaves and canopy structures, which can reflect the vegetation's ability to undergo biophysical processes such as photosynthesis, respiration and transpiration and is an important input variable for carbon cycle models, crop growth models and water cycle models [28]. Fractional vegetation cover (FVC) is an important parameter for many weather prediction models, regional and global climate models, hydrological models and land surface models and has been extensively used in agriculture, soil erosion risk evaluation, drought monitoring and environmental assessment [29]. Gross primary productivity (GPP) denotes the production ability of vegetation's photosynthesis [30]. In order to fully explore and utilize the information contained by vegetation, ecological parameters including the LAI, FVC and GPP were selected as the evaluation indicators in this study. In addition, land use and land cover (LULC) data were selected to distinguish between the vegetation ecosystem types.  
Due to their better performance in spatiotemporal coverage and validation accuracy compared with other similar satellite products, LAI data were selected from China's National Key R&D Program (Grant Number: 2020 YFE0200700) and the FVC and GPP data were selected from Global Land Surface Satellite (GLASS) products. The LAI data, with a spatial resolution of 1 km and temporal resolution of quarterly, were generated from MODIS data and meteorological data using a BP neural network algorithm. The GLASS FVC and GPP data were obtained from the University of Maryland ([[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)) (accessed on 12 December 2022)) with a 500 m spatial resolution and 8-day temporal resolution in 2016 and 2020. The larger the LAI, FVC and GPP value, the better the vegetation growth state and the better the ecosystem quality. In addition, the LULC is also an important factor in ecosystem quality assessment [17], which reflects ecosystem types. The LULC data were also selected from China's National Key R&D Program (Grant Number: 2020 YFE0200700) with a spatial resolution of 1 km in 2016 and 2020, which were reclassified based on first-level land cover, including cultivated land, forest, grassland, shrub, wetland, tundra and non-vegetation types. The land cover data were generated using object-based multi-scale segmentation and change detection classification technology based on geographical region and sample migration. The overall validation accuracy by selecting points on high-resolution images was 88.97%, and the Kappa coefficient was 0.73 [27].  
Ultimately, the ecosystem quality assessment was conducted at a 1 km spatial resolution and annual temporal resolution. So, the FVC and GPP indicators at 500 m of spatial resolution were resampled to 1 km using the nearest neighbor method. In addition, the LAs with a quarterly temporal resolution and FVC with an 8-day temporal resolution were composited to annual datasets using the maximum value composite (MVC) method.