## 2 Materials and Methods
### Data Source and Preprocessing  
The data sources of this study mainly include NPP data, human activities data, and meteorological data. The version of the NPP data is MOD17A3 HGF v006 and was obtained from the Land Processes Distributed Active Archive Center ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)), accessed on 20 December 2021), with the spatial resolution of 500 m [46]. In addition, this data product contains a data quality control file (NPP_QC), which indicates the quality reliability of NPP products in different regions. We conducted statistical analysis based on the NPP_QC data from 2000-2019, and the results showed that the multi-year average reliability of medium and high-level NPP data quality reached 90.32% in the Yangtze River Delta region from 2000 to 2019 [47]. The LULC data were obtained from the Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center (LAADS DAAC, [[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)), accessed on 2 March 2020), for MCD12Q1 v006 data, from 2001 to 2019. The population density [48] and GDP data (all with 1 km spatial resolution) were provided by the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences ([[https://www.resdc.cn](https://www.resdc.cn)]([https://www.resdc.cn](https://www.resdc.cn)), accessed on 5 March 2022). We downloaded the digital elevation model (DEM) data [49] (GDEMV3 30M) with 30 m spatial resolution from the Geospatial Data Cloud ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)), accessed on 5 March 2022), and they were processed using ArcGIS10.7 software to obtain slope and aspect data. The data sources for the spatial data used in this study are shown in Table 1.  
Figure 2: Characteristics of Interannual Variation of Precipitation and Temperature in the Yangtze River Delta (Note: ‘PRE’ means total annual precipitation, ‘TEM’ means annual average temperature).  
The meteorological data are obtained from the monthly dataset of China's terrestrial climate information provided by the National Meteorological Information Center ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn)), accessed on 10 February 2022), which mainly includes monthly average temperature, total precipitation, and total sunshine hours from 94 meteorological stations in and around the Yangtze River Delta region. The data period was 2000-2019, and the annual average data of climate factors at each station were obtained by aggregating monthly data, and the thin plate spline method was used to spatially interpolate the meteorological data within the region. All the above data were converted to raster data with the same projection coordinate system and 500m spatial resolution in ArcGIS 10.7 after a series of data pre-processing, including mosaic, projection transformation, resampling, and clipping. In addition, in order to meet the needs of the geographical detector input variable types (i.e., categorical variables), each driving factor was discretized and classified into nine categories using the natural breaks method [50], as shown in Table 2. The natural breaks, also called the Jenks natural breaks classification method, is a data clustering method designed to determine the best arrangement of values into different classes. This is done by seeking to minimize each class's average deviation from the mean value of the class, while maximizing each class's deviation from the means of the other classes. In other words, the method seeks to reduce the variance within classes and maximize the variance between classes. Therefore, it is very applicable to the categorization of the various driving factors.