## 2 Study Area and Data
### Data and Preprocessing  
The basic data of Yuecheng Reservoir mainly included geographic data and statistical data. Specifically, the geographic data comprised remote sensing multispectral images, digital elevation model (DEM) data, and precipitation and soil type data; the statistical data included fertilizer application, crop area, phenology, and yield information (Table 1). After considering the factors of space, time, and spectral resolution we chose Sentinel-2 images for use as the remote sensing images in this experiment. The Sentinel-2 satellite carries a multispectral imager, which can cover 13 spectral bands with a width of 290 km. The spatial resolutions of different spectral bands are 10 m, 20 m, and 60 m, respectively. Sentinel-2A and Sentinel-2B satellites complement each other, and the revisit cycle can reach 5 days. We chose appropriate months for cultivated land and crop type mapping according to crop phenology. Specifically, high-quality Sentinel-2 images with few or no clouds were selected during the period from January to December 2019. The downloaded Level 2A Sentinel-2 data were orthophoto-, geometric precision-, and atmosphere-corrected; \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **Data Description** & **Data Source** & **Data Acquisition Time** \\ \hline Remote sensing images & Sentinel-2 images & [[https://scihub.copernicus.eu/dhud/](https://scihub.copernicus.eu/dhud/)]([https://scihub.copernicus.eu/dhud/](https://scihub.copernicus.eu/dhud/))\#/home (accessed on 1 October 2020) & 2019 \\ \hline DEM & ASTER GDEM & [[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)) (accessed on 15 October 2020) & 2009 \\ \hline GPP & MOD17A2 & [[https://ladsweb.modaps.eosdis.nnas.gov/](https://ladsweb.modaps.eosdis.nnas.gov/)]([https://ladsweb.modaps.eosdis.nnas.gov/](https://ladsweb.modaps.eosdis.nnas.gov/)) (accessed on 14 July 2021) & 2019 \\ \hline Precipitation data & TRMM & [[https://ladsweb.modaps.eosdis.nnas.gov/](https://ladsweb.modaps.eosdis.nnas.gov/)]([https://ladsweb.modaps.eosdis.nnas.gov/](https://ladsweb.modaps.eosdis.nnas.gov/)) (accessed on 14 July 2021) & 2019 \\ \hline Soil Type data & Spatial distribution data & [[http://www.fao.org/home/en/](http://www.fao.org/home/en/)]([http://www.fao.org/home/en/](http://www.fao.org/home/en/)) (accessed on 10 March 2021) & 2009 \\ \hline Fertilizer statistics & Statistical data concerning & Statistical yearbook of Shanxi Province in 2019 & \\  & fertilization amounts among & Statistical yearbook of Henan Province in 2019 & 2019 \\  & counties in the study area & Statistical yearbook of Hebei Province in 2019 & \\ \hline Administrative division & County boundary in the study & [[https://www.webmap.cn/](https://www.webmap.cn/)]([https://www.webmap.cn/](https://www.webmap.cn/)) (accessed on 1 & \\ boundary & area & September 2020) & 2017 \\ \hline \multicolumn{4}{c}{DEM: digital elevation data; GPP: gross primary productivity.} \\ \end{tabular}
\end{table} 
Table 1: Basic data and their sources. thus, pre-processing only comprised splicing and clipping. Because we could not find high-quality images with few or no clouds in the region in February 2019, we interpolated images from adjacent months, namely January and March 2019, to obtain the February image. DEM data and soil type data required pre-processing that included splicing, clipping, and resampling to a resolution of 10 m. GPP data were based on the MOD17A2 dataset provided by the National Aeronautics and Space Administration (NASA), which is an 8-day synthetic product of GPP with a spatial resolution of 500 m. These data were preprocessed by batch clipping and resampling. Tropical rainfall measuring mission (TRMM) precipitation data were based on the TRMM 3B42 daily dataset provided by NASA, which has a spatial resolution of 0.25\({}^{\circ}\).