Mapping 10 m monthly surface water dynamics in the Yangtze River basin from 2017 to 2020 using a robust ATMC algorithm

Surface water bodies are irreplaceable in the global climate and ecological system. However, quantifying the extent and change of these water bodies has been challenging due to environmental complexity, climatic diversity, and their highly dynamic nature in seasons and interannual periods. Most established remote-sensing water extraction methods involve a practical trade-off between spatial and temporal resolution. To remedy these deficiencies, we proposed a simple and robust Automatic Threshold-based Multi-source data Combination algorithm to improve the efficiency and accuracy of water mapping, achieving monthly stable periodic monitoring. The algorithm fully considered the principles and advantages of Sentinel-1/2 images for water detection and performed adaptive optimal threshold calculation based on the Edge Otsu algorithm. A new product of Surface Water Dynamics in the Yangtze River Basin was generated on a monthly temporal scale and 10-m spatial scale from 2017 to 2020 using the algorithm based on the Google Earth Engine platform. The confusion matrix based metrics based on sample points were 95.85 % (producer's accuracy), 99.16 % (user's accuracy), 97.41 % (overall accuracy), 0.95 (Matthews correlation coefficient), and 0.95 (Kappa coefficient). Furthermore, the overall correct extraction rate of water bodies can reach 95.85 % and 91.87 % for small water bodies, with commission and omission errors of 0.04 % and 0.03 % at the sub-pixel scale, respectively. The distribution of water bodies within the basin is highly uneven, and their changes exhibit a distinct seasonal trend. The maximum inundation extent occurred in August 2020 (summer, 53,372.88 km2), while the minimum inundation extent was observed in November 2019 (winter, 42,491.74 km2). The successful application of the algorithm in the Yangtze River Basin established a good benchmark for surface water monitoring of multi-source satellite observations and showed great potential for global-scale applications. It will assist the monitoring and evaluation services of the UN Sustainable Development Goals and provide solutions to more global and regional issues.