The Loess Plateau is one of the most fragile ecological regions in China due to its shortage of water resources and severe soil erosion. The rapid development of urbanization and the implementation of the project of returning farmland to forest (grass) have caused the ecological environment of the region to be strongly impacted by human activities. It is necessary to investigate the spatial and temporal evolution characteristics of ecosystem services and trade-off/synergy relationships on the Loess Plateau, to achieve scientific management of ecological services and sustainable development of the region. This study quantitatively assesses three ecosystem services of water yield (WY), carbon storage (CS), and soil conservation (SC) on the Loess Plateau under different scenarios from 2000 to 2030 by using the InVEST and PLUS models. Further, the trade-off and synergistic relationships among the ecosystem services have been investigated by Spearman correlation analysis. The results showed that the land-use differences are more obvious under different policy scenarios, with a sharp expansion of constructed land, a gradual increase of forest land, and a continuous decrease of arable land in the Loess Plateau from 2000 to 2020; the water yield and soil conservation increase from 2000 to 2020, and the carbon storage shows an opposite trend. The soil conservation and carbon storage scenarios are the best under the ecological conservation scenario in 2030, while the water yield service is the best under the economic development scenario. There is a synergistic relationship between CS and SC, while there is a trade-off relationship between CS and WY. In addition, there are significant trade-off effects between SC and WY. These results can support guiding land-use management and ecological restoration.

The overexploitation and misuse of natural resources in oaseshave put a significant strain on the ecosystem’s fragility. Therefore, a rigorous study of the ecological environment’s quality is required to assure the sustainability of oasis growth. The GEE platform has the features of timeliness and large data cloud processing, which accelerating the development of the remote sensing ecological index. The MODIS data of the research region from 2000 to 2020 were uploaded online to the GEE platform in order to calculate the humidity, greenness, dryness, and heat indices for each year. Principal component analysis was then used to develop the remote sensing ecological index after normalization. In addition, Pearson correlation coefficient, Moran’s I index, geo-detector, and the MK trend test were employed to determine the dependability of the RSEI comprehensive index, analyze the ecological environment status and its change trend in the Aksu River Basin from 2000 to 2020, and investigate the external driving factors of RSEI spatial heterogeneity. (1) The average correlation degree of RSEI is as high as 0.820, and the Moran’s I index is larger than 0.9118; thus, its practicability, dependability, and spatial rationality are enhanced. (2) The natural environment quality of Aksu basin is impacted in two ways by human influences. (1) The adoption of ecological protection measures to support the growth of groundwater reserves in the Aksu basin, increase plant covering, and so improve and enhance the ecological environment’s quality. Following the adoption of ecological protection measures, the average RSEI rose by 12.89%, the ecological quality of the farmland-based region improved considerably, and the quality of the ecological environment was enhanced. (2) Urban growth inhibits environmental progress. The acceleration of urbanization and the large rise in NDBSI have exerted pressure on the development of RSEI, while the growth of cities and towns has decreased the vegetation cover in urban areas and impeded the improvement of ecological environment quality. (3) Both human and environmental causes contribute to the regional variability of RSEI in Aksu Basin. The geographical heterogeneity is mostly caused by temperature and land use, with land use being the most important driver. Strengthening research on the connection between groundwater storage change, land use, vegetation cover, and NDBSI may facilitate the growth of regional green economies.

Net primary productivity (NPP) is a critical indicator for evaluating the carbon sequestration potential of an ecosystem and regional sustainable development, as its spatiotemporal dynamics are jointly influenced by natural and anthropogenic factors. This study investigated the Sangong River Basin, an inland watershed located in northwestern China. By employing the Carnegie–Ames–Stanford Approach (CASA) model and the Geodetector method, integrated with remote sensing data and field surveys, we systematically analyzed the spatiotemporal evolution and driving mechanisms of NPP from 1990 to 2020. Our results reveal an average annual basin-wide NPP increase of 2.33 g C·m⁻²·a⁻¹, with plains experiencing significantly greater increases (2.86 g C·m⁻²·a⁻¹) than mountains (1.71 g C·m⁻²·a⁻¹). Land use intensity (LUI) explained 31.44% of the NPP variability in the plains, whereas climatic factors, particularly temperature (71.27% contribution rate), primarily governed the NPP dynamics in mountains. Soil properties exhibited strong associations with NPP. Specifically, a 1 g·kg⁻¹ increase in soil organic content elevated NPP by 99.04 g C·m⁻²·a⁻¹, while a comparable rise in soil salinity reduced NPP by 123.59 g C·m⁻²·a⁻¹. These findings offer spatially explicit guidance for ecological restoration and carbon management in arid inland basins, underscoring the need for a strategic equilibrium between agricultural intensification and ecosystem conservation to advance carbon neutrality objectives.

Soil organic carbon (SOC) plays a crucial role in the terrestrial carbon cycle and climate regulation. Quantifying the sensitivity of SOC to climate change is essential for developing effective strategies to address climate change and optimizing agricultural production. This study compares the performance of four machine learning models in assessing SOC, ultimately selecting the optimal Extreme Gradient Boosting model for spatial predictions of surface SOC (0–30 cm) across the country. The results indicate that areas with higher organic carbon density are primarily concentrated in the Tibetan Plateau and northeastern regions. Notably, regions with high uncertainty in predictions correspond to areas of elevated organic carbon density. Average temperature, average precipitation, and the Normalized Difference Vegetation Index were identified as the most influential factors across all models. Based on the predictions from the optimal model and a bottom-up framework, various potential climate change scenarios were considered, allowing for the quantification of SOC sensitivity to climate change. Under scenarios of increased temperatures and decreased precipitation, SOC loss intensified, hindering SOC accumulation. When the average temperature rose by 1.45 °C and precipitation decreased by 14.67%, a loss of 10% in SOC was projected for most regions of China. These findings provide critical insights for the proactive formulation of climate adaptation strategies, soil health preservation, and the maintenance of ecosystem stability.