As an important part of the intertidal ecosystem， tidal flats have unique environmental regulation service functions and ecological benefits such as maintaining coastline stability， accelerating material exchange and promoting carbon cycle. Accurate and timely assessment of the status of intertidal wetlands is essential to achieving sustainable management goals. With the help of Google Earth Engine （GEE） cloud computing platform， this paper uses the 2020 Sentinel-2 dense time series remote sensing images， integrates the Maximum Spectral Index Composite algorithm （MSIC） and the Otsu algorithm （Otsu） to construct a multi-layer decision tree classification model， so as to realize the rapid and automatic extraction of Australian intertidal tidal tidal flats. After vectorization， the spatial distribution dataset of high-resolution intertidal flats in Australia in 2020 was obtained， and the extracted tidal flats area was 10 708.22 km²， with an overall accuracy of 95.32% and a Kappa coefficient of 0.94. The dataset is stored in.shp format， with a temporal resolution of years， a spatial resolution of 10 m， and a data volume of 154 m. This data is suitable for coastline management， marine ecological research， environmental protection and monitoring， etc. The data can promote and manage coastal ecosystems， such as mangrove afforestation and control of alien species invasion such as Spartina alterniflora， and can also be used as basic data for scientific research， such as biodiversity， carbon storage estimation and sea level rise caused by sea level erosion etc.