## 2 Materials and Methods
### Data Sources and Processing  
This study uses a variety of data, including land use, soil, meteorological, and socioeconomic data, as follows (Table 1):  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date Type** & **Time Range** &
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & **Source** \\ \hline \multirow{3}{*}{CNLUCC} & 1990, 1995, 2000, & \multirow{3}{*}{30 m} & Resources and Environmental \\  & 2005, 2010, 2015, & & \\  & 2020 & & ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/))) accessed on \\ \cline{1-1} \cline{3-4}  & & & 9 January 2024 \\ \hline \multirow{3}{*}{Meteorological} & \multirow{3}{*}{1991–2020} & \multirow{3}{*}{10 km} & China Meteorological Data Network \\  & & & ([[https://data.cma.cn/](https://data.cma.cn/)]([https://data.cma.cn/](https://data.cma.cn/))) accessed on \\ \cline{1-1}  & & & 12 January 2024 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data description.  
(1) Land use data: This primarily includes the China Multi-Temporal Land Use and Land Cover Remote Sensing Monitoring Dataset (CNLUCC) for 1990, 1995, 2000, 2005, 2010, 2015, and 2020. This dataset serves as a reference for expanding training samples and verifying the proposed methods. The data uses Landsat remote sensing images as the primary information source and is constructed through manual visual interpretation.  
(2) Meteorological and soil data: Meteorological data includes monthly maximum temperature, average temperature, minimum temperature, precipitation, relative humidity, number of rainy days per month, wind speed, and solar radiation. Based on the monthly data of these eight key plant growth factors, this study interpolates meteorological data to a resolution of 10 km. Finally, this study clips the national interpolated meteorological  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date Type** & **Time Range** & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & **Source** \\ \hline Statistical data & 1991–2020 & - & \begin{tabular}{c} Guangdong Statistical Yearbook \\ ([[http://stats.gd.gov.cn/gdtjn/](http://stats.gd.gov.cn/gdtjn/)]([http://stats.gd.gov.cn/gdtjn/](http://stats.gd.gov.cn/gdtjn/))) \\ accessed on 22 January 2024 \\ \end{tabular} \\ \hline DEM & - & 30 m & \
\begin{tabular}{c} Shuttle Radar Topography \\ Mission data \\ ([[http://strm.csi.cgiar.org/strmdata/](http://strm.csi.cgiar.org/strmdata/)]([http://strm.csi.cgiar.org/strmdata/](http://strm.csi.cgiar.org/strmdata/))) \\ accessed on 9 January 2024 \\ \end{tabular} \\ \hline Soil data & - & 1 km &
\begin{tabular}{c} Resources and Environmental \\ Science Data Center \\ ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/))) accessed on \\ 9 January 2024 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Overview of the study area.  
data according to the administrative boundaries of Guangdong Province to obtain the meteorological data for the entire province. These interpolated monthly meteorological data are obtained based on daily observation data of meteorological element stations at more than 2400 stations nationwide and based on calculating the monthly values of each meteorological element. Soil data includes attributes such as composition, type, entire profile depth, and water-holding capacity. The Digital Elevation Model (DEM) is obtained from the Shuttle Radar Topography Mission (SRTM), a collaborative project between NASA and the National Geospatial-Intelligence Agency (NGA), providing coverage for all of China at a spatial resolution of 30 m.  
(3) Socio-economic data: Actual yield data for various prefecture-level cities in Guangdong Province from 2000 to 2020, as well as population and economic data from 1991 to 2020, are all sourced from the Guangdong Statistical Yearbook.