Accurately assessing the impact of land use patterns on total phosphorus (TP) concentration in surface water is crucial for protecting the water environment of the Yangtze River Basin (YRB). However, due to the heterogeneity of land use patterns, the regional differences in the intensity and direction of their impacts on TP concentrations in the YRB remain insufficiently understood. This study utilizes water quality monitoring data from state-controlled sections in 2021 and employs spatial autocorrelation analysis, geographic detectors, and Pearson correlation models to identify the impacts of land use on TP concentrations at multiple scales across the YRB. The results indicate that TP concentrations at 98.8% of the monitoring stations in the YRB exceed the Class III standard, with high concentrations of TP concentrated in the Pudu River Basin, Chengdu Plain, Jianghan Plain, and Yangtze River Delta regions. At the YRB scale, the spatial pattern of built-up land, cropland, and industrial and mining land significantly increases TP concentrations, while the pattern of forest and grassland areas exert mitigating effects. At the sub-basin scale, the impact of land use patterns on TP concentrations varies regionally. Specifically, TP concentrations in the Pudu River Basin are primarily attributed to the spatial pattern of industrial and mining land, in the Chengdu Plain to the spatial pattern of cropland and industrial and mining land, and in the Jianghan Plain to the spatial pattern of cropland, built-up land, and industrial and mining land. These results provided decision support for TP concentration control strategies and effective mitigation measures.

Accurate assessment of cropland maps is crucial for ensuring food security, effective agricultural management, and environmental monitoring. With the widespread application of high-resolution (≤10 m) remote sensing imagery and the advancement of machine learning techniques, numerous high-resolution cropland maps have been developed. However, comprehensive evaluations of their accuracy remain limited. We utilized 163,861 validation samples and national land survey statistical data to conduct a multi-scale comparison of the accuracy of seven cropland maps (one 1 m and six 10 m maps) in China. Additionally, five synergistic strategies were employed to generate more accurate fused cropland maps. Validation results showed that the overall accuracy (OA) of the seven maps ranged from 0.79 to 0.91, with ESA-WorldCover (ESA-WC) exhibiting the highest OA, followed by AI Earth China land cover classification dataset (AIEC), ESRI Land Cover (ESRI-LC), and Cropland Use Intensity in China (China-CUI), while Sino-LC1 showed the lowest performance. Spatially, ESA-WC achieved the highest accuracy in nearly 60% of provinces, followed by AIEC and ESRI-LC, each accounting for approximately 20%. AIEC performed best in western provinces, whereas ESRI-LC dominated in the middle and lower reaches of the Yangtze River. Area consistency assessments revealed that, on average, the seven maps overestimated cropland areas by 20% compared to statistical data. Among these, ESA-WC showed the highest proportion of provinces with relative errors within ±20%, but this proportion was only 50%. Moreover, the OA of the fused maps exceeded 0.92, with county-level R² values compared to statistical data reaching 0.98, significantly improving the reliability of cropland products in over 60% of provincial administrative regions. Based on these results, effective synergistic strategies for high-resolution cropland mapping are proposed.

Accurate and timely information on tobacco cultivation is essential for macro-level regulation, tobacco monopoly management, and farmer support in China. Remote sensing offers an efficient means for mapping tobacco, yet most studies focus on mid- to late-season identification, while early-season detection remains underexplored. Therefore, we integrated multi-source remote sensing data with the Google Earth Engine (GEE) platform to quantify separability between tobacco and competing crops, determined the optimal early-season phenological window and discriminative features, and developed an early-season tobacco mapping approach using a stacked generalization ensemble. The results show that January–February period provides the most effective early identification window for Changting County, with near-infrared (NIR) and water-sensitive features contributing most to class discrimination. The stacked ensemble achieved an overall accuracy of 87.17%, with producer and user accuracies of 85.97% and 92.28%, respectively, outperforming individual classifiers. At the township scale, the mapped tobacco area was strongly associated with tobacco purchase volumes (R² = 0.819), supporting the reliability of the derived maps. Spatially, tobacco fields exhibited a pronounced corridor-like pattern, primarily influenced by proximity to water systems and terrain conditions. The proposed workflow provides tobacco management agencies with timely, spatially explicit information to support precise and intelligent tobacco production management.

Water functional zoning plays a crucial role in water resource allocation, pollution prevention, and ecological protection. With the increasing intensity of human activities, there is a significant mismatch between current water functional zoning and the economic, social development needs and ecological protection goals. Existing water functional zoning methods mainly rely on expert experience for qualitative judgment, which is highly subjective and inefficient. In response, this paper presents a transferable quantitative feature system and introduces a machine learning-based progressive zoning framework for water functions, validated through a case study of the Yangtze River Basin. The results show that the overall accuracy of the framework is 0.78, which is 4–7% higher compared to traditional single models. In terms of spatial distribution, the transformation of protection and reserved zones in 2020 mainly occurred in the middle and lower reaches, where human activities are frequent, particularly in Sichuan and Jiangxi provinces. The development zones are highly concentrated in the downstream areas, with some regions transitioning into protection or reserved zones, mainly in Hubei and Chongqing provinces. Adjustments to buffer zones are primarily concentrated along inter-provincial boundary areas, such as the junction between Hubei and Anhui provinces. This framework helps managers quickly identify key areas for optimizing water functional zones, providing valuable reference for the precise management of water resources and the formulation of ecological protection strategies in the basin.