A dataset of annual surface water distribution in the growing season on the Mongolia Plateau from 2013 to 2022

Abstract

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.