Surface water is a crucial resource and environmental element for human survival and ecosystem stability; therefore, accurate information on the distribution of surface water bodies is essential. Extracting this information on a large scale is commonly implemented using moderate- and low-resolution satellite images. However, the detection and analysis of more detailed surface water structures and small water bodies necessitate the use of very high-resolution (VHR) satellite images. The large-scale application of VHR images for water extraction requires convenient and accurate methods. In this paper, a method combining a pixel-level water index and image object detection is proposed. The method was tested using 2018/2019 multispectral 4-m resolution images obtained from the Chinese satellite Gaofen-2 across Beijing, China. Results show that the automatic extraction of water body information over large areas using the proposed method and VHR images is feasible. Kappa coefficient and overall accuracy of 0.96 and 99.8% after post-classification improvement were obtained for testing images inside the Beijing area. The Beijing water body dataset obtained included a total of 489.53 km² of surface water in 2018/2019, 108.01 km² of which were ponds with an area smaller than 2 km². This study can be applied for water body extraction and mapping in other large regions and provides a reference for other methods for using VHR images to extract water body information on a large scale.

Investigating the spatial distribution and dynamics of terrestrial carbon storage is vital for climate change mitigation. However, horizontal spatial analyses often overlook heterogeneity in complex terrains. Here, we focused on the Gonghe Basin on the northeastern margin of the Qinghai–Tibet Plateau, where resource exploitation and ecological conservation interact. By using land use and DEM data and integrating the InVEST model, Geoda, and a geographical detector, we showed the altitudinal gradient effect and spatiotemporal evolution of carbon storage in the Gonghe Basin from 2000 to 2020 and identified the key factors influencing these patterns. Results show the following: (1) From 2000 to 2020, carbon storage in the Gonghe Basin exhibited a distinct pattern of “high at mid-elevations, low at both summit and valley” along the elevation gradient. High-value areas were concentrated in the forest–grassland zone between 2800–4400 m, while low-value areas were distributed in the human activity-intensive zone of 2100–2800 m and the alpine desert zone of 4400–5000 m. (2) The synergistic drivers of carbon storage differed markedly across elevation gradients. The low-elevation zone (2100–2800 m) was characterized by strengthened interactions between vegetation cover and precipitation as well as human activity variables, indicating a coupled natural–anthropogenic driving regime. In the mid-elevation zone (2800–4400 m), interactive effects shifted from vegetation–natural factor coupling to enhanced synergy with social factors such as population density. In the high-elevation zone (4400–5000 m), stable long-term interactions between vegetation and temperature predominated, while sensitivity to interactions involving human activity factors increased. (3) Although natural factors remained dominant, the explanatory power of human activity factors—including GDP density, land-use intensity, and grazing intensity—increased over time across all elevation gradients, suggesting progressively stronger human intervention in carbon cycling. (4) Based on these findings, this study proposes a “three belts–three strategies” synergistic governance framework—“regulation and restoration” for the low-elevation belt, “conservation and efficiency enhancement” for the mid-elevation belt, and “monitoring and early warning” for the high-elevation belt—aiming to enhance regional carbon sink capacity and ecological resilience through zone-specific, targeted interventions. These findings offer a scientific basis for reinforcing regional ecological security and improving carbon sink management.

Net solar radiation is an essential parameter that characterizes surface energy exchange and plays a critical role in climate change, solar power generation, and agricultural irrigation. Although the global GLASS surface all-wave daily net radiation (NR) product exhibits high overall accuracy, a comprehensive quality assessment for continental China remains lacking, resulting in unclear regional applicability. Therefore, this study focuses on mainland China. Based on solar net radiation observations from 50 meteorological stations (2000–2016) and 37 ecological stations (2000–2020), four evaluation metrics were used: the correlation coefficient (R), mean bias error (MBE), root mean square error (RMSE), and coefficient of determination (R²). The results indicate that, during the study period, GLASS NR showed relatively small deviations from the observed values across most regions of China, with significant discrepancies observed only in southern Yunnan, Guangdong, Guangxi, and Hainan. Seasonally, GLASS NR performed better in autumn and winter than in spring and summer. Interannually, there was only a slight decline in data quality for a few individual years; however, overall, an upward trend was observed. Regarding land cover types, GLASS NR accuracy was lower for shrublands, forests, and grasslands, whereas it performed better for other land cover types. Overall, the GLASS NR product demonstrates high accuracy and good temporal continuity across mainland China. However, significant regional variations exist, and localized applications require optimization and refinement. This study provides valuable insights for improving net radiation products across multiple spatiotemporal scales.

Large-scale agricultural remote sensing monitoring is challenged by pronounced spatial heterogeneity arising from fragmented terrain, complex climatic backgrounds, and diverse cropping structures. However, existing agricultural zoning schemes generally lack an integrated consideration of remote sensing imaging mechanisms and key variable conditions such as atmospheric interference and crop phenology, limiting their direct utility in guiding region-specific sensor selection and classification algorithm calibration. To address this limitation, this study integrates multi-source earth observation data and agricultural statistical information to construct an Agricultural Remote-sensing Classification Difficulty Index (ARCDI) from multiple dimensions, including image availability, cropping structure, cropland fragmentation, and topographic environment. On this basis, a graph theory-based spatially constrained Skater clustering algorithm is introduced to establish a two-tier “cropland–major cereal crops” zoning framework oriented toward remote sensing applications. The results indicate that the proposed framework delineates five distinct first-tier cropland classification difficulty zones across China. This zoning scheme effectively quantifies the regional heterogeneities in monitoring challenges. Building upon this first-tier zoning, the framework is further refined into 50 second-tier major cereal crop classification difficulty zones, including 13 winter wheat zones, 21 maize zones, and 16 rice zones. Statistical tests and spatial analyses demonstrate that the proposed zoning scheme significantly outperforms conventional clustering approaches in balancing within-zone homogeneity and spatial continuity. This advantage is quantitatively reflected by consistently lower residual spatial autocorrelation (residual Moran’s I ≈ 0.10–0.11) and an approximately 20% reduction in within-zone variance compared with other spatially constrained methods. Extensive field-sample validation provides preliminary evidence of an inverse relationship between crop-type classification difficulty and accuracy. These results confirm the framework’s reliability in identifying regional difficulty and its decision-support value for selecting remote sensing strategies. Overall, this study systematically elucidates the spatial differentiation patterns of remote sensing classification difficulty for cropland and major cereal crops across China. The proposed framework provides robust scientific support for data selection, algorithm optimization, and differentiated strategy formulation in national-scale agricultural monitoring, thereby facilitating the operationalization of regional agricultural remote sensing applications.