Uncovering the trade-offs and synergy relationship of multiple ecosystem services (ESs) is important for scientific ecosystem management and the improvement of ecological service functions. In this study, we investigated the spatiotemporal changes of four typical ES types (i.e., water yield (WY), carbon storage (CS), soil conservation (SC), and habitat quality (HQ)) from 2001 to 2020 in the Han River Basin (HRB). Meanwhile, the trade-offs and synergies between paired ESs and the socioecological drivers of these ESs were further explored. The results showed that grassland, cropland, and bare land decreased by 12,141.3 km², 624.09 km², and 22.1 km² during the study period, respectively, which can be attributed to their conversion to forests in the HRB. Temporally, the WY, CS, and SC all showed a continuously increasing trend. Spatially, WY and HQ exhibited bipolar clustering characteristics, with WY exhibiting low-value clustering in the upstream and high-value clustering in the downstream, while CS showed the clustering characteristics of a scattered distribution of cold and hot spots from 2001 to 2020. The spatial patterns of aggregation locations in CS and HQ were relatively similar, with clusters of higher ES values mainly distributed in the western and central regions and clusters of lower ES values mainly located in the eastern and southeastern regions, while the aggregation of WY was spatially concentrated. Overall, the CS showed a significant positive correlation with HQ, but a significant negative correlation with WY. Spatially, WY and HQ, CS, and SC showed a substantial trade-off relationship in the northwest and southeast parts of the study area, while HQ, CS, and SC mainly exhibited a synergistic relationship in most parts of the study area. Slope and temperature had high influencing factor coefficients on multiple ESs; the mixed effect of terrain and natural factors was significantly greater than the impact of a single factor on ESs, and terrain factors played an essential role in the changes in ESs. The findings can provide technical and theoretical support for integrated scientific ecosystem management and sustainable development at the local scale.

Soil erosion remains a critical ecological challenge on China’s Loess Plateau (LP), where fragile geomorphology and intensive human activities jointly amplify land degradation risks. As land-use and land-cover change (LUCC) is a primary determinant of erosion processes, clarifying the nexus between land patterns and erosion intensity is essential for formulating effective conservation strategies. This study integrates the Chinese Soil Loss Equation (CSLE) with the Patch-generating Land Use Simulation (PLUS) model to analyze the spatiotemporal dynamics of soil erosion from 2000 to 2020 and project future patterns for 2060 under five scenarios: Natural Development (ND), Ecological Protection (EP), Economic Development (ED), Cropland Protection (CP), and Planning Guidance (PG). Results indicate a fluctuating decline in LP soil erosion during 2000–2020, marked by a transition toward predominantly slight erosion (~70% of the total area), while high-intensity erosion remained concentrated in central and western cropland and grassland. Scenario projections reveal pronounced divergence in erosion outcomes. The EP scenario, characterized by sustained vegetation expansion, demonstrated the highest efficacy in erosion mitigation. Conversely, the ED scenario exhibited the most severe erosion risk due to urban expansion into ecological areas. The PG scenario effectively reconciled the trade-offs between ecological conservation and socioeconomic demands, maintaining a balanced erosion control performance. In the context of global climate change, the complexity of soil and water conservation governance is expected to intensify. This study suggests that future efforts should focus on scientifically guiding the evolution of land-use patterns through sustainable spatial planning. Furthermore, targeted engineering and biological conservation measures must bae implemented for high-risk land categories to ensure the long-term stability of the regional ecological security barrier.

Fragmented farmland and persistent cloud–snow interference in the high-altitude cold regions of the Qinghai–Tibet Plateau, coupled with unstable crop phenology, pose significant challenges for accurate cereal and oil crop identification using single-date imagery or low-dimensional features. This study focused on the agricultural areas of the Shigatse River Valley in the Qinghai–Tibet Plateau. Leveraging the Google Earth Engine (GEE) cloud computing platform, we integrated Sentinel-2 remote sensing data with field survey sampling data to extract the planting structures, distribution patterns, and cultivated areas of cereal and oil crops. Three machine-learning classifiers—Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosted Trees (GBT)—were evaluated to investigate the influence of different feature sets and classifier combinations on mapping accuracy. The results indicated that when all feature bands were utilized, the RF classifier achieved the highest performance, with an overall accuracy of 84.77% and a kappa coefficient of 0.64, outperforming both the SVM and GBT models. The incorporation of phenological and topographic features further enhanced classification accuracy, providing a robust framework for identifying cereal and oil crops in high-altitude environments. Based on the optimal model estimation, the cultivated areas in 2021 were 581.52 km² for highland barley, 295.39 km² for wheat, and 386.81 km² for rapeseed. Their spatial patterns closely aligned with the valley-terrace topography and local irrigation conditions. These findings offer novel insights and a reliable methodology for the rapid extraction of crop spatial information in regions with complex planting structures.