Under the pressure of SDG15.3.1 compliance, it is imperative to solve the land salinization degradation problem in the Yellow River Basin as China’s granary. From the view of geographical scale, six zoning units were divided in the Yellow River Basin with ‘climate-meteorology-geomorphology’ as the main controlling factor, and a salinization inversion model was constructed for each zoning unit. Appropriate surface parameters were selected to construct a three-dimensional feature space according to the individual geographical zones. Based on the cloud data processing capability of the Google Earth Engine platform, a feature space inversion process was applied for automatic inversion of salinization. Salinization distribution maps of the Yellow River Basin in 2015 and 2020 were obtained at 30 m resolution by classifying the salinization inversion result. The distribution and spatiotemporal variation of salinization as well as the causes of salinization were analyzed. Reasonable prevention and control suggestions were subsequently proposed. This study could also be scaled up to larger and more complex geographical regions.

China's Yellow River Delta represents a typical area with moist semi-humid soil salinization, and its salinization has seriously affected the sustainable use of local resources. The use of remote sensing technology to understand changes in the spatial and temporal patterns of salinization is key to combating regional land degradation. In this study, a feature space model was constructed for remote sensing and monitoring land salinization using Landsat 8 OIL multi-spectral images. The feature parameters were paired to construct a feature space model; a total of eight feature space models were obtained. An accuracy analysis was conducted by combining salt-loving vegetation data with measured data, and the model demonstrating the highest accuracy was selected to develop salinization inversion maps for 2015 and 2020. The results showed that: (1) The total salinization area of the Yellow River Delta displayed a slight upward trend, increasing from 4244 km2 in 2015 to 4629 km2 in 2020. However, the area's salting degree reduced substantially, and the areas of saline soil and severe salinization were reduced in size; (2) The areas with reduced salinization severity were mainly concentrated in areas surrounding cities, and primarily comprised wetlands and some regions around the Bohai Sea; (3) Numerous factors such as the implementation of the “Bohai Granary” cultivation engagement plan, increase in human activities to greening local residential living environments, and seawater intrusion caused by the reduction of sediment contents have impacted the distribution of salinization areas in the Yellow River Delta; (4) The characteristic space method of salinization monitoring has better applicability and can be promoted in humid-sub humid regions.

Drought represents one of the most critical environmental hazards in arid and semi-arid regions, exerting profound impacts on ecological security and sustainable development. Nevertheless, many existing drought indices exhibit delayed responses to precipitation and soil moisture anomalies, thereby constraining their ability to characterize the rapid onset and evolution of drought events. To address this limitation, we propose the Standardized Temperature–Vegetation Drought Index (STVDI), which integrates precipitation, evapotranspiration, temperature, and vegetation dynamics within a Euclidean space framework and explicitly incorporates lag-response analysis. Taking the Mongolian Plateau (MP)—a key transition zone from taiga forest to desert steppe—as the study region, we constructed a 1 km resolution STVDI dataset spanning 2000–2021. Results reveal that over 88% of the MP is highly susceptible to flash droughts, with an average lag time of only 0.52 days, underscoring the index’s capacity for rapid drought detection. Spatial analysis indicates that drought severity peaks during March and April, with moderate drought conditions concentrated in central Mongolia and severe droughts prevailing across southwestern Inner Mongolia. Although trend analysis suggests a slight long-term alleviation of drought intensity, nearly 70% of the MP is projected to experience further intensification in the future. This study delivers the first high-resolution, low-lag drought monitoring dataset for the MP and advances theoretical understanding of drought propagation and lag mechanisms in arid and semi-arid ecosystems.

Mongolia Plateau is located in arid and semi-arid areas, and hydrology and water resources are important constraints for the development of its resources and environment. Grasping the temporal and spatial distribution of water bodies on the Mongolian Plateau is of great significance for indicating the temporal and spatial characteristics of water resources and the water environment and their impacts on and responses to regional climate change as well as disaster prevention and reduction. However, as the vast Plateau spans both China and Mongolia, it is a great challenge to accurately and automatically obtain large-scale and long time series water bodies at the basin scale. In this research, we adopted the method of combining local deep learning training and Google Earth Engine (GEE) distributed computing to endow GEE with deep learning computing capabilities so that GEE could rapidly and automatically deploy deep learning models. Based on this, we obtained the distribution of surface water in the growing season of the Mongolia Plateau from 2013 to 2022 with a spatial resolution of 30 meters. 5,000 verification points were manually selected, and the overall verification rate was 88.0%. The dataset is in the form of TIFF grid, containing 28 tile images of with 5°×5°×10 years, with a data volume of 339 MB (88.1 MB compressed, 189 GB in RAW). The data volume in the raw format is 189 GB. With the method used in this dataset, users can automatically and efficiently map water bodies in the cloud platform, which makes it possible to automatically and efficiently process large-scale and long-time series water bodies in arid and semi-arid regions. This is a valuable dataset for application and promotion.