Controlled-release urea (CRU) fertilizers are widely used in agricultural production to reduce conventional nitrogen (N) fertilization-induced agricultural greenhouse gas emissions (GHGs) and improve N use efficiency (NUE). However, the long-term effects of different CRU fertilizers on GHGs and crop yields in vegetable fields remain relatively unexplored. This study investigated the variations in GHG emissions at four growth stages of lettuce in the spring and autumn seasons based on a five-year field experiment in the North China Plain. Four treatments were setup: CK (without N application), U (conventional urea—N application), ON (20% reduction in urea—N application), CRU (20% reduction in polyurethane-coated urea without topdressing), and DCRU (20% reduction in polyurethane-coated urea containing dicyandiamide [DCD] without topdressing). The results show that N application treatments significantly increased the GHG emissions and the lettuce yield and net yield, and DCRU exhibited the lowest N₂O and CO₂ emissions, the highest lettuce yield and net yield, and the highest lettuce N content of the N application treatments. When compared to U, the N₂O emission peak under CRU and DCRU treatments was notably decreased and delayed, and their average N₂O emission fluxes were significantly reduced by 10.20–20.72% and 17.51–29.35%, respectively, leading to a significant reduction in mean cumulative N₂O emissions during the 2017–2021 period. When compared to U, the CO₂ fluxes of DCRU significantly decreased by 8.0–16.54% in the seedling period, and mean cumulative CO₂ emission decreased by 9.28%. Moreover, compared to U, the global warming potential (GWP) and greenhouse gas intensity (GHGI) of the DCRU treatment was significantly alleviated by 9.02–17.13% and 16.68–20.36%, respectively. Compared to U, the N content of lettuce under DCRU was significantly increased by 6.48–17.25%, and the lettuce net yield was also significantly increased by 5.41–7.71%. These observations indicated that the simple and efficient N management strategy to strike a balance between enhancing lettuce yields and reduce GHG emissions in open-field lettuce fields could be obtained by applying controlled-release urea containing DCD without topdressing.

Competition for land is increasing as demand for multiple land uses and ecosystem services rises. Land regulation of the principles of landscape ecology is necessary to develop more sustainable approaches to land use planning. The research evaluated the present land patterns and determined best practices for its regulation of Dongwang Township in Quyang County, located in the Taihang Mountain area of Hebei Province, China. The research used the landscape ecology theory to construct an index system for landscape pattern analysis based on the GIS and Fragstats 3.3 software. In this study, we examined the specific reasons that landscape ecology is superior to traditional methods in land consolidation planning and design, which is conducive to the comprehensive development of land ecological benefits. Landscape ecological planning can effectively reduce landscape fragmentation and improve intensive management. The result found that the descending order of the Shannon index was current landscape, landscape ecological planning, and traditional planning. Landscape ecological planning could protect the natural diversity than traditional planning. Landscape ecological planning enables the creation of long corridors, with higher densities and connectivity and lower average corridor widths than traditional planning. Besides, it can improve ecological service function values in the study area to varying degrees, thus discouraging residents from limiting themselves to grain production. This research has great potential to improve the visibility of ecosystem services in local land use planning and, thus, to improve the ecological functioning of future landscapes.

The growing problem of non-grain production of cultivated land (NGPOCL) has increased food security risk, garnering attention from China and other nations worldwide. Current research predominantly focuses on the internal planting structure of cultivated land. To more comprehensively measure the level of NGPOCL, we categorized NGPOCL into two types: visible non-grain production of cultivated land (VNGPOCL) and invisible non-grain production of cultivated land (INGPOCL). VNGPOCL and INGPOCL scopes were extracted utilizing land use and vegetation index data, exploring their spatial–temporal characteristics and driving factors through spatial feature analysis and multiple linear regression methods. The findings are as follows: (1) The degree of VNGPOCL shifted from mild to moderate, with its rate increasing from 5.16% in 2000–2010 to 10.82% in 2010–2020. Furthermore, the spatial variation in VNGPOCL indicated a growing east–west disparity while showing a reduction in north–south differences, reflecting significant spatial agglomeration effects. (2) There was a dramatic increase in areas classified as having moderate to severe INGPOCL, with the rate rising from 14.24% in 2000 to 41.47% by 2020. The east–west and north–south disparities concerning INGPOCL diminished rapidly, also indicating strong spatial agglomeration effects. (3) The driving factors for VNGPOCL and INGPOCL differed significantly depending on developmental stages. The results contribute valuable insights into accurately characterizing the spatial–temporal features associated with NGPOCL in Hebei Province while enhancing risk management strategies related to NGPOCL.

Sustainable cultivated-land governance requires spatial information that is accurate, traceable, and suitable for expert review. This study developed a human-in-the-loop GeoAI–GIS workflow that transforms high-resolution semantic segmentation outputs into image-derived land-cover objects, transparent review priorities, and structured spatial evidence records. The Cultivated Land Information Management Transformer (CLIM-Former), trained on LoveDA and transferred to Gaofen-2 imagery of Mancheng District without target-area fine-tuning, provided the semantic basis for downstream object construction. Connected-component analysis generated measurable objects with semantic, geometric, locational, contextual, and provenance attributes, while interpretable rules prioritized objects for review and deterministic templates produced standardized records and verification case sheets. Across three training seeds, the guard-enabled downstream configuration achieved a mean mIoU of 56.27 ± 0.10% and an agriculture IoU of 63.12 ± 0.90%. In a reproducible 100-tile subset, 3352 objects were retained, and 1705 were prioritized. Expert acceptance was 83.00% for 100 sampled prioritized objects and 37.00% for 100 non-prioritized controls, corresponding to a 2.24-fold enrichment with a 95% confidence interval of 1.71–2.94 and a significant between-group difference according to Fisher’s exact test at p < 0.001. The generated records achieved 100% key information coverage, 95.05% factual consistency, and a mean suitability score of 4.28/5. The workflow provides a transparent and reviewable link between remote-sensing interpretation and cultivated-land information management while preserving expert authority over management decisions.

High-resolution remote sensing semantic segmentation requires the joint modeling of local details, global semantics, and height-derived geometric structures, and it provides an important basis for urban object mapping, land-cover analysis, and fine-grained spatial understanding. However, in complex urban scenes, fine-grained boundaries, small objects, inter-class similarity, and spectral confusion can still weaken the stability of pixel-level prediction. To enhance discriminative dense feature representations in high-resolution remote sensing images, we propose HDSMNet, a dual-branch multimodal semantic segmentation network designed for optical–nDSM data. The network separately extracts appearance and semantic features from optical imagery and height–structural features from nDSM, and introduces a Height-Guided Sparse Cross-Modal Fusion (HGSCF) module. Rather than treating nDSM as an additional feature source for generic fusion, HGSCF derives contextual representations, local feature contrasts, and structural-discontinuity cues from encoded nDSM features and uses them to guide sparse anchor-based interaction between optical and height features. This design enhances discriminative dense feature representations through interaction with a compact set of geometry-guided anchors. To complement HGSCF at the output stage, HDSMNet further adapts a Context-Guided Refinement (CGR) path that combines intermediate-response-guided contextual aggregation with dynamic feature modulation. This supplementary path recalibrates decoder features for output refinement. Experiments on the ISPRS Potsdam and Vaihingen datasets show that HDSMNet achieves mIoU values of 86.57% and 84.22%, respectively; ablation results further identify HGSCF as the main contributor to the observed improvement.

Accurately estimating village-level winter wheat yield in coastal saline–alkali farmland is challenging because this region has strong spatial differences and multiple environmental stresses. In this study, Huanghua City, Hebei Province, was selected as a typical coastal saline–alkali area. Sentinel-2 images, climate factors, and topographic variables, including elevation, topographic wetness index, distance to the coastline, and distance to water systems, were combined to build a phenology-guided feature set for winter wheat yield prediction in coastal areas. The results showed that Phenology-Guided Feature Integration XGBoost achieved an R² of 0.6382 and an RMSE of 450.15 kg/ha, which was slightly better than Gradient Boosting (R² = 0.6256) and Random Forest (R² = 0.6098), and clearly better than SVR (R² = 0.4792), Ridge regression (R² = 0.4582), and a single Decision Tree (R² = 0.3088). Then, a three-stage branch was designed to identify the main drivers of SI, NDVI, and winter wheat yield at different stages, helping explain how environmental constraints and vegetation responses jointly affect final yield. The Three-Stage Fusion XGBoost Model achieved an R² of 0.6439, an RMSE of 446.24 kg/ha, and an MAE of 363.38 kg/ha, showing a slight improvement in prediction accuracy. SHAP analysis showed that SI, distance-related factors, elevation, TWI, and NDVI were important drivers of winter wheat yield variation. Spatial prediction results showed higher winter wheat yield in inland areas (5145 kg/ha) and lower yield in coastal areas (4198 kg/ha). This framework supports village-scale winter wheat yield prediction in coastal saline–alkali farmland and improves model interpretability.