Changes in the land use/cover alter the Earth system processes and affect the provision of ecosystem services, posing a challenge to achieve sustainable development. In the past few decades, the Yellow River (YR) basin faced enormous social and environmental sustainability challenges associated with environmental degradation, soil erosion, vegetation restoration, and economic development, which makes it important to understand the long-term land use/cover dynamics of this region. Here, using three decades of Landsat imagery (17,080 images) and incorporating physiography data, we developed an effective annual land use/cover mapping framework and provided a set of 90 m resolution continuous annual land use/cover maps of the YR basin from 1986 to 2018 based on the Google Earth Engine and the Classification and Regression Trees algorithm. The independent random sampling validations based on the field surveys (640 points) and Google Earth (3456 points) indicated that the overall accuracy of these maps is 78.3% and 80.0%, respectively. The analysis of the land system of the YR basin showed that this region presents complex temporal and spatial changes, and the main change patterns include no change or little change, cropland loss and urban expansion, grassland restoration, increase in orchard and terrace, and increase in forest during the entire study period. The major land use/cover change has occurred in the transitions from forests, grasslands, and croplands to the class of orchard and terrace (19.8% of all change area), which not only increase the greenness but also raised the income, suggesting that YR progress towards sustainable development goals for livelihood security, economic growth, and ecological protection. Based on these data and analysis, we can further understand the role of the land system in the mutual feedback between society and the environment, and provide support for ecological conservation, high-quality development, and the formulation of sustainable management policies in this basin, highlighting the importance of continuous land use/cover information for understanding the interactions between the human and natural systems.

Changing evapotranspiration (ET) will impact freshwater availability, knowledge of which is a critical prerequisite for policy development related to water resources management in an evolving climate, especially for water‐limited regions. However, the socio‐economic effects are not considered due to the lack of detailed information about this. Here we used a well‐validated remote sensing model and multiple socio‐economic factors to investigate the driving factors of ET changes over the Loess Plateau during 1982–2012. Results showed that the modeled annual ET significantly increased by ~2 mm yr⁻² during this period (p < 0.001), caused by increased transpiration (2.16 mm yr⁻²) and interception (0.27 mm yr⁻²), which was partly offset by decreased soil evaporation (−0.47 mm yr⁻²). Meanwhile, although the average ET of the forest was larger (480.4 ± 14.8 mm yr⁻¹), it was found that the change in total ET of the region was dominated by that in grassland and cropland (1.1 km³ yr⁻², 90% altogether). Factorial simulations indicated that the intensifying ET over 79.4% and 9.1% of the study area can be explained by vegetation greening and climate change, respectively. Further analysis suggested that the vegetation greening and the increased ET were primarily associated with the rapid urbanization and agricultural intensification. Our findings highlight the potential unfavorable effects of socio‐economic activities on water resources management on this coupled natural‐human system that is already facing water scarcity issues.

Understanding the net ecosystem CO₂ exchange (NEE) between terrestrial ecosystems and the atmosphere is crucial for accurate estimation of carbon budgets, which remains unclear for grassland ecosystems. Here we upscaled site‐level NEE from 44 grassland flux towers (1,457 site‐months) to the global scale by using a model tree ensemble approach that considers the management activities (grazing and cutting) (MTE‐GM). Cross‐validation showed that MTE‐GM performs reasonably well in terms of among‐site variability and seasonal variation, with a Nash‐Sutcliffe efficiency of 0.90 and 0.86 and an R² of 0.91 and 0.86, respectively. Radiation (shortwave and longwave), temperature, leaf area index, and fraction of absorbed photosynthetic active radiation had the highest relative explanatory power in predicting NEE. Based on MTE‐GM, mean annual NEE of global grassland was 72 ± 4 g C m⁻² year⁻¹ (1.9 ± 0.11 Pg C year⁻¹) during 1982–2011, suggesting that the grassland ecosystems have been acting as a small carbon source during the past three decades. However, grasslands in temperate and continental regions had the largest carbon sink of −61.9 ± 5.7 and −51.8 ± 7.9 g C m⁻² year⁻¹, respectively. Moreover, we found that elimination of grassland management effect resulted in an extra emission of 1.7% CO₂ to the atmosphere (CO₂ sink from the management is 0.03 Pg C year⁻¹). From the 1980s to the 2000s, 38% (22%) and 17% (18%) of pixels showed an increased (decreased) carbon uptake and decreased (increased) carbon release, respectively. Uncertainty assessment suggested that there would be higher confidence in NEE estimates in most parts of middle‐ to high‐latitude regions in the Northern Hemisphere.