## 2 Study Area and Data Source
### Data  
This paper explores the impact of climate, topography, LCC, and socio-economic factors on FVC. Climate impacts includes rainfall, sunshine duration, temperature, pressure, and humidity, whereas topological variables are elevation, slope, and aspect. Additionally, urban land, water bodies, and unused land were selected as LCC factors. Gross domestic product per capita, income of rural residents, population, gross industrial output value, local fiscal revenue, fixed asset investment, and total retail sales of consumer goods are socio-economic factors related to FVC. Table 1 describes the data sources and variables used in this study.  
Google Earth Engine (GEE) makes it available to process a large number of satellite imagery quickly and accurately with planetary-scale analysis capabilities. More than thirty years of historical imagery and scientific datasets are updated and expanded daily in GEE. Vast amounts of dataset derived from The Moderate Resolution Imaging Spectroradiometer (MODIS) can be accessed online based on the GEE platform, which includes The Normalized difference vegetation index (NDVI), Leaf area index (LAI), Gross Primary Productivity (GPP), and other derived datasets. In our study, the NDVI product (MOD13Q1) was selected from 2000-2015 at a 250-m spatial resolution and 16d intervals. These products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows.  
Climate change has a direct or indirect impact on vegetation cover. Meteorological data from 26 weather stations in the study area from 2000-2015 were obtained from the Chinese Meteorological Science Data Center, which includes time-series data for rainfall, sunshine duration, temperature, pressure, and humidity (Table 1). In order to fully reflect the impact of climate factors on vegetation coverage, the Inverse Distance Weighting method was used to interpolate the monthly climate data. Then, we calculated the county-level climate factor data set--derived from the raster of meteorological factors--in Guangdong. The mountainous topography of southern China influences regional vegetation distribution differentiation [28]. We used the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) to calculate topological data.  
Vegetation cover changes as land function caused by land cover dynamics changes. Thus, the information on non-vegetation land was summarised for each county, and included urban land, water bodies, and unused land. The landcover data used in this research cover four years (2000, 2005, 2010, and 2015) and were collected by the Ministry of Land and Resources of China. Compared  
Figure 1: (**a**) Location of southern China within China; (**b**) Location of Guangdong in southern China; (**c**) Elevation map of the study area.  
with natural and human factors, socio-economic factors have a significant impact on vegetation cover change at different levels of administrative divisions. The socio-economic factors of each county in this study were primarily selected from the China Statistical Yearbook provided by the National Bureau of statistics; other county data are selected from the statistical yearbook of prefecture level cities from 2000-2015.