Amid persistent global food security challenges, the efficient utilization of cultivated land resources has become increasingly critical, as optimizing Cultivated Land Utilization Efficiency (CLUE) is paramount to ensuring food supply. This study introduced a cultivated land utilization index (CLUI) based on Fractional Vegetation Cover (FVC) to assess the spatiotemporal variations in Henan Province’s CLUE. The Theil–Sen slope and the Mann–Kendall test were used to analyze the spatiotemporal variations of CLUE in Henan Province from 2000 to 2020. Additionally, we used a genetic algorithm optimized Artificial Neural Network (ANN) and a particle swarm optimization-based Random Forest (RF) model to assess the comprehensive in-fluence between topography, climate, and human activities on CLUE, in which incorporating Shapley Additive Explanations (SHAP) values. The results reveal the following: (1) From 2000 to 2020, the CLUE in Henan province showed an overall upward trend, with strong spatial heterogeneity across various regions: the central and eastern areas generally showed decline, the northern region remained stable with slight increases, the western region saw significant growth, while the southern area exhibited complex fluctuations. (2) Natural and economic factors had notable impacts on CLUE in Henan province. Among these factors, population and economic factors played a dominant role, whereas average temperature exerted an inhibitory effect on CLUE in most parts of the province. (3) The influenced factors on CLUE varied spatially, with human activity impacts being more concentrated, while topographical and climatic influences were relatively dispersed. These findings provide a scientific basis for land management and agricultural policy formulation in major grain-producing areas, offering valuable insights into enhancing regional CLUE and promoting sustainable agricultural development.

Climate change and human activities increasingly impact watershed ecological quality (EQ), particularly in large transboundary watersheds. This study introduces an amended water benefit-based ecological index (AWBEI) to assess changes in EQ within the Irtysh River Basin from 2000 to 2023. AWBEI integrates indicators such as surface water abundance, latent heat, land surface temperature, vegetation, and soil indices. We used Google Earth Engine to efficiently process these data. The spatiotemporal analysis showed significant spatial variation in EQ across the basin. Areas north of 55 °N consistently had better EQ. Overall, the basin exhibited a slight improvement trend, although with notable variations. The study identified persistent significant degradation in 5.43% of the area, primarily in central and western regions. Driving factor analysis using the optimal parameters-based geodetector model identified temperature ( q- values 0.583–0.695) and precipitation ( q- values 0.561–0.665) as primary natural influences, with GDP ( q- values 0.431–0.480) as the main socio-economic factor. Strong interactions between these drivers were observed, emphasizing the need for integrated ecological management approaches that consider both direct effects and synergistic interactions among environmental and socioeconomic factors.

Wetlands are vital for global carbon storage, yet face significant pressures. This study quantifies wetland landscape pattern changes and their impact on carbon storage in the transboundary Irtysh River Basin (IRB) from 2000 to 2020, identifies key landscape drivers, and projects future carbon storage under distinct scenarios for 2030. We utilized multi-temporal land cover data (GWL_FCS30), landscape metrics (Fragstats), the InVEST model for carbon storage estimation, interpretable machine learning (NGBoost coupled with SHAP analysis) to link landscape patterns to carbon dynamics, sensitivity analysis, and the PLUS model for scenario-based future projections (Natural Scenario - S1, Wetland Protection - S2, Wetland Degradation - S3). From 2000 to 2020, total wetland area increased by 10,417 km², primarily driven by marsh and swamp expansion, resulting in a net carbon storage increase from 2.827 × 10⁸ tC to 2.885 × 10⁸ tC (net gain: 5.8 × 10⁶ tC). Sensitivity analysis revealed high responsiveness (Sensitivity Index = 10.812) of carbon storage to wetland area change. The NGBoost model accurately predicted carbon storage based on landscape metrics (MSE = 0.682, RMSE = 0.8259, MAE = 0.7811). SHAP analysis identified the aggregation index (AI), largest patch index (LPI), and number of patches (NP) as the most critical landscape predictors influencing carbon storage. Future projections for 2030 estimate total carbon storage at 3.229 × 10⁸ tC under S1 (stabilization), increasing to 3.421 × 10⁸ tC under S2 (protection), but declining sharply to 1.871 × 10⁸ tC under S3 (degradation). Landscape structure, particularly aggregation and the extent of large patches, significantly influences wetland carbon storage in the IRB. Proactive wetland protection policies are crucial for enhancing and maintaining carbon sequestration capacity in this sensitive transboundary basin, contributing to regional climate change mitigation efforts. Graphical abstract This graphical abstract summarizes a study on wetland dynamics and carbon storage in the Irtysh River Basin (IRB) from 2000 to 2020, with 2030 projections, focusing on wetland conservation and carbon sink roles. Using GWL_FCS30 land use data, Fragstats 4.2, InVEST, NGBoost with SHAP, and the PLUS model, the study analyzes landscape patterns, estimates carbon storage, identifies key drivers (e.g., aggregation index, largest patch index), and simulates future scenarios. Results show marsh and swamp expansion, increased fragmentation, and carbon storage rising to 2.885 × 10⁸ tC by 2020. Projections for 2030 estimate 3.229 × 10⁸ tC (Natural Scenario), 3.421 × 10⁸ tC (Wetland Protection), and 1.871 × 10⁸ tC (Wetland Degradation). This visual highlights the importance of wetland conservation for enhancing carbon sequestration and informs sustainable land-use strategies in transboundary basins

As a transboundary river with rich and unique wetland types, the Irtysh River faces various challenges and threats from human activities and climate change, which affect area, type, and function of wetland. To accurately obtain information on the spatial and temporal distribution of wetlands in this basin, this study compares and evaluates the consistency and accuracy of a total of eleven remote sensing (RS) based land use/land cover (LULC), and wetland products. The information extraction effect of each RS product was examined through methods such as wetland area and type description, thematic map comparison, and similarity coefficient and Kappa coefficient calculations, which can reflect the wetland distribution characteristics and differences among the RS products in the Irtysh River Basin. The results show that although there is a consensus among the products in the major wetland distribution areas, there are still obvious deviations in detail depiction due to differences in factors such as data sources and methods. The products of Global 30 m Wetland Fine Classification Data (GWL_FCS30) and Global 30 m Land Cover Data (GLC_FCS30-2020) released by the Institute of Space and Astronautical Information Innovation (ISAI) of the Chinese Academy of Sciences (CAS) have a clear advantage in extracting spatial morphology features of wetlands due to the use of multi-source data, while the Esri Global 10 m Land Cover Data (ESRI_Global-LULC_10m) and products such as the global 10 m land cover data (FROM_GLC10_2017) from Tsinghua University have higher classification consistency. Moreover, data resolution, classification scheme design, and validation methods are key factors affecting the quality of wetland information extraction in the Irtysh River Basin. In practical terms, the findings of this study hold significant implications for informed decision-making in wetland conservation and management within the Irtysh River Basin. By advancing wetland monitoring technologies and addressing critical considerations in information extraction, this research effectively bridges the gap between remote sensing technology and practical applications, offering valuable insights for regional wetland protection efforts.