Soil organic matter (SOM) scale effects are critical for crop growth and food security, especially in coal–grain complexes. However, few studies describe the spatial variation in SOM and its influencing factors at different sampling scales. Here, geostatistical theory and mathematical statistical methods were adopted to analyze the spatial variation characteristics of and structural differences in SOM in the coal mining subsidence area at Zhaogu No. 2 Mine at different sampling scales. The results showed that SOM varied spatially at large, medium, and small scales, and the coefficients of variation were 28.07%, 14.93%, and 14.31%, respectively, which are moderate values. The characteristic functions of the SOM content at different sampling scales differed, and the spatial structure scale effect was obvious. The spatial distribution of the SOM content fitted by the multiscale fitting model method was generally the same as the spatial distribution law of the SOM content fitted by the single scale kriging interpolation method; however, in terms of the detailed expression and spatial distribution of small-scale SOM content, the fitting model method was more accurate, and the accuracy increased by 36%. At the different sampling scales, sample size and soil type had specific effects on the SOM spatial distribution. These results provide research concepts and technical countermeasures for improving food security and the ecological environment in the coal–grain complex and help ensure sustainable agricultural lands.

Soil nitrogen is very important for crop growth and development. However, the factors affecting the three-dimensional spatial distribution of soil total nitrogen (TN), particularly in coal mining subsidence areas, are unclear. In this study, Markov geostatistics was used to analyse the three-dimensional spatial distribution characteristics and influential factors of TN by examining 180 soil samples from the Zhaogu mine in China. The results showed that the TN content was significantly different at different soil depths (0–20, 20–40, 40–60 cm) and decreased with increasing soil depth. The variation coefficient of the TN content decreased gradually from top to bottom, ranging from 18.18 to 25.62%. In addition, the TN content was greatly affected by mining subsidence, rainfall, irrigation, fertilization and management mode. The factors that influenced the TN content also varied across different slope positions. The TN content of the upslope was the highest, and the TN content of the middle slope was the lowest. These results can provide research ideas and technical countermeasures for ecological environment improvement and sustainable land development in coal mining subsidence areas.

Frequent seasonal phase transitions in cold and arid lakes require different remote sensing indices for frozen and open-water periods, complicating the use of traditional empirical indices for automated monitoring. To address this challenge, this study proposes an intelligent indexing framework integrating the heuristic reasoning of Large Language Models (LLMs) with Random Forest (RF) feature selection. Leveraging the Google Earth Engine (GEE) and Landsat 8 data from Ulansuhai Lake, five LLMs such as Gemini and ERNIE were employed to generate candidate spectral indices based on typical sample spectra. Optimal band combinations were identified via RF importance, and Land Surface Temperature (LST) was incorporated as a physical constraint for unified cross-seasonal classification and determine the optimal threshold. Results show that the LLM-derived ERNIE-WISI and Gemini-WISI exhibit high robustness. During the freezing period, ERNIE-WISI significantly outperformed other indices, achieving an Overall Accuracy (OA) of 89% and a Kappa of 0.86. Spatially, it yielded snow and ice mapping with clear textures and low commission errors. During the non-freezing period, ERNIE-WISI achieved an OA of 95% with a Kappa of 0.84. While Gemini-WISI achieved an OA of 94% with a Kappa of 0.80, performing comparably to MNDWI. Notably, ERNIE-WISI effectively suppressed background interference in complex landscapes like narrow channels and aquaculture areas, maintaining high geometric fidelity and spatial continuity. A key advantage of ERNIE-WISI is its consistent performance without seasonal threshold adjustments. Aligned with the AI for Science paradigm, this methodology bridges AI-driven heuristic discovery and physical remote sensing, offering a robust, transferable solution for long-term dynamic lake monitoring in extreme environments, thereby facilitating sustainable water management.

Improving water resource use efficiency (WRUE) is essential for achieving sustainable water management under increasing socioeconomic and environmental pressures. This study investigates the spatiotemporal evolution, associated factors, and spatial transition characteristics of WRUE across 11 provincial-level administrative regions in the Yangtze River Basin during 2010–2024. An integrated framework combining the super-efficiency SBM-window DEA model, Malmquist–Luenberger index, GeoDetector, and conventional and spatial Markov chain models was developed to characterize efficiency dynamics, productivity changes, explanatory factors, and state-transition pathways. The results showed that WRUE exhibited an overall fluctuating upward trend with a clear spatial gradient of lower reaches > middle reaches > upper reaches. The mean ML index was 1.004, indicating that technological change (TC) was the main contributor to productivity improvement. Urbanization rate, water use per CNY 10,000 of GDP, industrial water-use share, and primary-industry share exhibited relatively high explanatory power, and their interactions enhanced explanatory power. Markov analysis revealed strong persistence in WRUE states, while transition probabilities differed across spatial neighborhood conditions. Assuming stable transition probabilities, the high-efficiency state would reach a steady-state probability of 0.8155. These findings provide insights for differentiated water resource management and coordinated regional development.

Urban expansion has reshaped land-use patterns, altered the provision of ecosystem services, and brought challenges to regional sustainable development. However, studies on urban agglomerations with uneven development remain insufficient. This study takes the core development area of the Central Plains Urban Agglomeration as the study area and explores changes in ecosystem services during multidimensional urbanization from 2000 to 2020. Using the CASA and InVEST models, three ecosystem services, namely net primary productivity (NPP), water yield (WY), and soil conservation (SC), were quantified. Spatial associations and local heterogeneity were analyzed using the bivariate Moran’s I. The results show that regional urbanization exhibited a Zhengzhou-centered monocentric pattern, with rapid growth in GDP density and significant expansion of urban land. The responses of ecosystem services to urbanization showed divergent trends, with NPP increasing slightly, while WY and SC decreased. NPP and SC showed a synergistic effect, whereas WY had trade-off relationships with both services. Due to uneven regional development, urbanization indicators and ecosystem services showed evident spatially heterogeneous relationships. This study provides evidence for ecological conservation, ecosystem-service management, and sustainable spatial governance in developing urban agglomerations where rapid growth and ecological constraints coexist.

Information on urban dynamics is crucial for tracking expansion and investigating urbanization-related environmental impacts. Accurate and timely monitoring of long-term urbanization trends, however, presents significant challenges. Leveraging impervious surface area as a primary metric, this research analyzes spatio-temporal urbanization dynamics and landscape pattern responses in the Zhengzhou metropolitan region between 1990 and 2019, using a novel dual-analytical approach. The total ISA of Zhengzhou increased from 71.12 km ² in 1990 to 523.34 km ² in 2019, with an annual growth area of 15.59 km ² . There was significant positive spatial autocorrelation in the expansion speed of ISA between different regions, and the hot spots with rapid development varied from period to period, namely Erqi, Zhongyuan, and Huiji districts from 1990 to 2000, Huiji, Jinshui, and Guangchegn districts from 2000 to 2010, and Zhongyuan, Huiji, and Guancheng districts from 2010 to 2019. Overall, the impervious surface expansion presented a pattern dominated by the direction of "southeast to northwest". The ISP of each level increased or decreased to varying degrees during the study period, and the change of ISP levels indicated significant change of the pattern of urban landscape, and the overall landscape shape showed a trend from simple to complex. The depiction of the urban impervious surface development trajectory could provide a meaningful resource for urban managers and policy makers implement the national central city construction strategy in Zhengzhou metropolis.