Tidal flats are threatened by tidal reclamation and climatic changes around the world. Particular challenges exist in China where tidal flats are changing rapidly along with accelerated economic development in coastal regions. The unique and important ecosystem functions and services that tidal flats provide in coastal regions warrant the necessary of mapping such a particular land cover type in high precision and accuracy. Existing national tidal flat maps of China, which were derived from the 30-m resolution Landsat imagery and auxiliary data, are insufficient to support practical management efforts. In this study, in order to produce an accurate tidal flat map with finer spatial resolution, we employed 28,367 scenes of time series Sentinel-2 images acquired in 2019 and 2020 along the entire coastal line of China. The short revisit cycle (2-5 days) of the Sentinel-2 improved the opportunities of obtaining the highest and lowest tide images, and the finer spatial resolution (10-m) enhanced the capacity of precision tidal flat extraction. A rapid, robust, and automated tidal flat mapping approach is essential to large-scale applications. In this study, we developed an approach by integrating the maximum spectral index composite (MSIC) and the Otsu algorithm (OA), and so named MSIC-OA. By GEE platform, we automated the execution of MSIC-OA to Sentinel-2 images, and produced an up-to-date 10-m spatial resolution tidal flat map of China (China_Tidal Flat, CTF). Validated by massive field-based observations and selected edge-points, the CTF map achieved an overall accuracy of 95% and the F1 score of 0.93. As we calculated, the total area of tidal flats in China was 858,784 ha, and Jiangsu Province accounted the largest proportion (24%) of the national total. This study is the first attempt to delineate tidal flats automatically at a 10-m spatial resolution. The CTF map can provide essential information for management of coastal ecosystems and facilitate the implementations of coastal and marine related Sustainable Development Goals.

Tidal flats are threatened by tidal reclamation and climatic changes around the world. Particular challenges exist in China where tidal flats are changing rapidly along with accelerated economic development in coastal regions. The unique and important ecosystem functions and services that tidal flats provide in coastal regions warrant the necessary of mapping such a particular land cover type in high precision and accuracy. Existing national tidal flat maps of China, which were derived from the 30-m resolution Landsat imagery and auxiliary data, are insufficient to support practical management efforts. In this study, in order to produce an accurate tidal flat map with finer spatial resolution, we employed 28,367 scenes of time series Sentinel-2 images acquired in 2019 and 2020 along the entire coastal line of China. The short revisit cycle (2-5 days) of the Sentinel-2 improved the opportunities of obtaining the highest and lowest tide images, and the finer spatial resolution (10-m) enhanced the capacity of precision tidal flat extraction. A rapid, robust, and automated tidal flat mapping approach is essential to large-scale applications. In this study, we developed an approach by integrating the maximum spectral index composite (MSIC) and the Otsu algorithm (OA), and so named MSIC-OA. By GEE platform, we automated the execution of MSIC-OA to Sentinel-2 images, and produced an up-to-date 10-m spatial resolution tidal flat map of China (China_Tidal Flat, CTF). Validated by massive field-based observations and selected edge-points, the CTF map achieved an overall accuracy of 95% and the F1 score of 0.93. As we calculated, the total area of tidal flats in China was 858,784 ha, and Jiangsu Province accounted the largest proportion (24%) of the national total. This study is the first attempt to delineate tidal flats automatically at a 10-m spatial resolution. The CTF map can provide essential information for management of coastal ecosystems and facilitate the implementations of coastal and marine related Sustainable Development Goals.