Systematic method for mapping fine-resolution water cover types in China based on time series Sentinel-1 and 2 images

Humans have been transforming natural surface water bodies into various types of artificial water cover to meet the growing demand for food, energy, and flood protection in recent decades. These changes have exerted cumulative impacts on the water cycle, environment, and ecology. Although the geographical distribution of and changes in water cover types is essential to understanding these changes, timely and accurate acquisition and updating are difficult to accomplish, mainly because of the lack of a practical and effective classification method, especially at the global and national scales. From the perspective of water cover, first we suggest a comprehensive water cover classification system in terms of water sources, water body shapes, and water usage, including rivers, reservoirs, lakes, agricultural ponds, seasonal wetlands, and rice fields. An automatic and operational classification method based on shape features and flooding frequency (AuCoSaFF) is developed accordingly. Then, we employ this method to produce an unprecedented 10-meter spatial resolution China Water Cover Map (CWaC) using time series Sentinel-1 and Sentinel-2 images acquired in 2020 and obtained from the Google Earth Engine. The validation shows that this dataset has an overall accuracy of 86% and a kappa coefficient of 0.83. The CWaC is the first thematic mapping on water cover types over all of China, and it provides information on the complete water cover types. It indicates that China's water cover area is 240,383 km(2 ) of which natural water accounts for 56.3%. China's water bodies are mainly distributed in Heilongjiang, Tibet, and Qinghai provinces. A comparison with seven other water-related data products highlights the advantages of the AuCoSaFF in acquiring high spatial and temporal resolution information about the complete water cover types at large scales. The shape-based and inundation frequency-based automatic method underlines the potentials of fast water cover type mapping at large scales using existing water datasets. We also anticipate that this freely available dataset will provide evidence of the states of and changes in wetlands, help to improve our understanding of human influences on the hydrologic cycle, and inform water-management decision-making.

Automated summary

Humans have been transforming natural surface water bodies into artificial water cover to meet the growing demand for food, energy, and flood protection. These changes have cumulative impacts on the water cycle, environment, and ecology. However, the lack of a practical and effective classification method at a global and national scale makes it difficult to acquire timely and accurate water cover data.