Tea, recognized as one of the world’s three principal beverages, plays a significant role both economically and culturally. The accurate, large-scale mapping of tea plantations is crucial for quality control, industry regulation, and ecological assessments. Challenges arise in high-resolution imagery due to the spectral similarities with other land covers and the intricate nature of their boundaries. We introduce a Phenology-Guided SwinUnet (PGSUNet), a semantic segmentation network that amalgamates Swin Transformer encoding with a parallel phenology context branch. An intelligent fusion module within this network generates spatial attention informed by phenological priors, while a dual-head decoder enhances the precision through explicit edge supervision. Using Hangzhou City as the case study, PGSUNet was compared with seven mainstream models, including DeepLabV3+ and SegFormer. It achieved an F1-score of 0.84, outperforming the second-best model by 0.03, and obtained an mIoU of 84.53%, about 2% higher than the next-best result. This study demonstrates that the integration of phenological priors with edge supervision significantly improves the fine-scale extraction of agricultural land covers from complex remote sensing imagery.

Non-grain use of cultivated land (NGUCL) in ecologically fragile regions has become a major challenge to food security and land sustainability, yet its spatiotemporal dynamics, spatial spillover effects, and associated factors remain insufficiently understood. Taking Ningxia, China, as a typical semi-arid to arid transition zone, this study developed a phenology-informed framework that combined multi-temporal Landsat imagery, random forest classification, spatial autocorrelation analysis, centroid and standard deviation ellipse models, and a spatial lag model to identify and analyze NGUCL in 2005, 2010, 2015, and 2020. Within the cultivated land boundary, NGUCL was further decomposed into cash crop-cultivated farmland (CCCF) and farmland abandonment (FA). The results show that the classification framework achieved robust performance, with overall accuracies above 85% across the benchmark years. Food-crop mapping reached an OA of 86.38–90.12% and a Kappa of 0.80–0.85, while FA mapping reached an OA of 85.60–86.74% and a Kappa of 0.70–0.72. NGUCL in Ningxia exhibited strong subregional differentiation under the gradients of northern irrigation, central arid, and southern mountainous conditions. CCCF was more closely associated with irrigated and agriculturally productive areas, whereas FA was concentrated in ecologically constrained counties and showed stronger dispersion and migration complexity. Spatial econometric results further indicate significant spatial spillover effects, suggesting that NGUCL-related processes in one county are associated with those in neighboring counties. The effects of natural, socioeconomic, and agricultural production factors also varied by type and period, indicating that NGUCL in ecologically fragile regions is not a homogeneous land-use transition process. By distinguishing CCCF from FA, this study provides a more nuanced interpretation of NGUCL and offers empirical evidence for understanding cultivated land transition and governance in ecologically fragile areas.

Improving water use efficiency (WUE) is a crucial approach to addressing agricultural water scarcity and promoting the sustainability of agricultural ecosystems. Although extensive research has been conducted on the factors associated with cropland WUE, the relationship between cropland fragmentation and WUE remains poorly understood. This study focused on northern China and employed an integrated “size–shape–configuration” framework to measure cropland fragmentation from 2005 to 2020. A geographically weighted regression (GWR) model was then applied to analyze the spatially varying relationships between cropland fragmentation and WUE. The results showed that over half of northern China experienced intensified cropland fragmentation. Noticeable spatial heterogeneity was observed, with relatively low fragmentation in the North China Plain and the Northeast China Plain and higher levels in the topographically complex northwestern region. Throughout the study period, the size-based fragmentation index (FI_size) consistently exhibited higher mean values than the shape-based (FI_shape) and configuration-based (FI_config) indices. The mean cropland WUE across the 1027 analysis units increased from 0.962 to 1.071, although 220 units—mainly distributed in the three northeastern provinces—experienced a decline. Among the analysis units with statistically significant local coefficients, FI_config was predominantly negatively associated with WUE, whereas FI_size and FI_shape were predominantly positively associated with WUE, with the positive associations mainly concentrated in the North China Plain. Moreover, FI_config exhibited larger absolute local coefficient magnitudes and greater spatial variability than the other two fragmentation dimensions. Environmental factors also showed distinct spatial associations with WUE, with annual precipitation and NDVI being predominantly positively associated with WUE, whereas soil erodibility and slope were mainly negatively associated. These findings highlight the need for region-specific management of cropland landscape patterns. This study provides preliminary quantitative evidence of the relationships between cropland fragmentation and WUE, offering valuable insights for improving WUE and promoting sustainable agricultural management.