## 2 Materials and Methods
### GPP Measurement for Cropland Use Transition  
This study categorizes cropland use transitions into three categories: non-agriculturalization, cropland abandonment, and cropland compensation, and systematically analyzes their impact on GPP. Non-agriculturalization refers to converting cropland into construction land, forest land, garden land, grassland, water bodies, or other non-agricultural uses, ultimately disrupting local agricultural ecosystems and crop cultivation and production [29]. Cropland compensation refers to converting non-agricultural land types into cropland. The Green Chlorophyll Vegetation Index (GCVI) is an indicator that linearly correlates with the content of vegetation chlorophyll and the Leaf Area Index (LAI), effectively reflecting the growth status and chlorophyll content of vegetation. The GCVI maintains high sensitivity even in dense vegetation, unaffected by the saturation of LAI in thick canopies. Therefore, the GCVI can be used as a key indicator to effectively distinguish land use types (grassland, farmland, and forest) with different vegetation coverage in the study area [30; 31]. In this study, Landsat images processed via the Google Earth Engine are utilized to extract annual GCVI-max and GCVI-diff feature images. Then, CNLUCC data is combined with ground truth data from visual interpretation and a feature image library. On this basis, deep learning techniques are applied to generate a 30 m spatial resolution cropland distribution map for Guangdong Province from 1990 to 2020 [32]. After analyzing the annual changes in cropland, the spatial distribution of non-agriculturalization and cropland compensation is obtained. Finally, the Global Agro-Ecological Zones model (GAEZ) is used to estimate the GPP loss for each pixel (Figure 2). \[\mathrm{GPP_{\mathrm{ca}}}=\sum_{i=1}^{n}\mathrm{CNAS_{i}}\times\mathrm{ yield_{i}}\;\mathrm{i}=1,2,3,\ldots, \tag{1}\]  
\[\mathrm{GPP_{\mathrm{cc}}}=\sum_{i=1}^{n}\mathrm{CCS_{i}}\times\mathrm{ yield_{i}}\;\mathrm{i}=1,2,3,\ldots, \tag{2}\]  
where \(\mathrm{GPP_{\mathrm{ca}}}\) and \(\mathrm{GPP_{\mathrm{cc}}}\) represent the GPP loss caused by non-agriculturalization and the GPP from cropland compensation, respectively. \(\mathrm{CNAS_{i}}\) and \(\mathrm{CCS_{i}}\) represent the areas of non-agriculturalization and cropland compensation for the i-th pixel, respectively. The GAEZ model estimates the climatic suitability for growing specific crops based on climate conditions and then uses an empirical formula to calculate the crop's production potential, considering both irrigation and rain-fed scenarios [33]. Under rain-fed conditions, the influence of precipitation on crop yield is solely considered, while under irrigation conditions, it is assumed that water is sufficient, and thus the impact of water on crops is not considered [34]. Guangdong Province is a primarily rain-fed agricultural area. \(\mathrm{yield_{i}}\) represents the unit area GPP for the i-th pixel under rain-fed conditions, calculated using the GAEZ model. The main crops considered are rice and maize, accounting for about 90% of the total grain production in Guangdong Province. To accurately depict the impact of cropland use transitions on GPP, the climatic conditions, elevation, soil, and other factors for calculating \(\mathrm{yield_{i}}\) remain constant. The input land use data represents three different cropland use transitions. Additionally, the climate conditions are based on the average climate values from 1991 to 2020. Cropland abandonment refers to the process whereby previously cultivated land progressively reverts to natural vegetation due to a lack of agricultural management for a period of at least two to five years [35]. Based on the obtained annual cropland maps for 1990-2020, this study combines the difference between the annual Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI), along with NDVI feature images.