The Requisition–Compensation Balance of Cropland (RCBC) policy is important for ensuring food security. Previous studies have mainly focused on the quantity and quality of cropland when assessing the impacts of this policy. In terms of morphology, previous studies have primarily relied on landscape indicators. Therefore, this study aims to thoroughly analyze the impacts of the RCBC policy on the quality and morphology of cropland (especially morphological spatial pattern analysis, MSPA) in the Pearl River Delta (PRD) during 1996–2021. To this end, we constructed a comprehensive evaluation index system by combining MSPA, landscape indicators, and field research. The results show that the cropland quality in the PRD has exhibited a consistent improvement trend. High-quality cropland is spreading from central cities to the periphery, and the spatial distribution is becoming more even. Nonetheless, MSPA reveals an increasing trend of cropland fragmentation. The results indicate a decline in the area of the “core”, an increase in the area of the “island”, and a decrease in the connectivity of the cropland. Our field research confirms that the RCBC policy has indirectly exacerbated cropland fragmentation. In many regions of the PRD, the fragmentation of cropland hinders the application of agricultural mechanization and increases the cost of cultivation, resulting in severe cropland abandonment. Therefore, local governments should implement rigorous planning and prioritize cropland morphology when compensating cropland. Our findings are expected to provide empirical evidence for improving the RCBC policy and protecting cropland.

Mitigating nitrogen dioxide (NO₂) pollution is a critical objective for enhancing urban environmental quality. The spatial structure of urban tree canopies plays a crucial role in influencing NO₂ diffusion and deposition. However, previous studies have focused mainly on the linear relationships between two-dimensional green spaces and NO₂, while the associated nonlinear relationships and threshold effects of three-dimensional urban tree canopy (UTC) spatial structure remain underexplored. To address this gap, we leveraged 1 m resolution satellite-derived data and explainable machine learning (XGBoost, SHAP, PDP) to examine the nonlinear influences and threshold effects of three-dimensional UTC spatial structures on NO₂ in Shenzhen. The results revealed that urban tree canopy spatial structure is associated with NO₂ concentrations. Among the key metrics, the two-dimensional canopy coverage ratio (CCR) emerged as the primary canopy-related correlate of lower NO₂ concentrations, while three-dimensional vertical structure metrics, particularly canopy height variability (CHV) and standard deviation of canopy height (SDCH), acted as critical secondary correlates in modulating the spatial distribution of pollutants. Based on these relationships, we identified potential threshold ranges for key metrics by comparing mathematically identified inflection points with practical urban planning constraints. In summary, this study advances the spatial analysis of “green spaces-NO₂” interactions from a two-dimensional to a three-dimensional perspective. Our findings could provide quantitative guidance for optimizing green space structure in high-density urban areas to inform strategies potentially associated with improved NO₂ outcomes.