Abstract

Coastal zones are essential ecosystems due to their provision of invaluable ecosystem services. However, the geomorphologic characteristics of coastal zones are becoming more complex and changeable due to global warming, sea-level rise (SLR), and the intensification of anthropogenic activities. Therefore, accurate and timely knowledge of coastal land cover types (including tidal flats, coastal vegetation, and year-long water cover) is needed for coastal research and sustainable management. To date, land cover products for coastal areas are mainly derived from moderate resolution imaging spectroradiometer images, but few studies have used Sentinel-1 synthetic aperture radar (S1) and Sentinel-2 Multispectral Instrument (S2) images, which can provide more detailed maps. We developed a Rule-based Time Series Classification (RTSC) approach to map coastal land cover types at a 10 m resolution, combining S1/S2 time-series images (2015–2019) and Google Earth Engine (GEE). These products were developed for the coastal zone of the Yellow Sea (YS), East Asia, which is an essential ecosystem protecting a coastal population of 60 million people from storms and SLR effects. Accuracy assessment showed that the annual maps of coastal land cover had high overall accuracy. The coastal land cover types for the YS in 2019 comprised 3593.42 km2 of tidal flats, 28,506.98 km2 of coastal vegetation, and 5436.92 km2 of coastal year-long water. The interannual dynamics of the coastal land cover area in the YS during 2015–2019 were smaller. This study provides a promising method that combines S1/2 time series, a RTSC approach, and GEE to map coastal land cover areas at large scales. The 10 m resolution maps generated in this study are the most current dataset of coastal land cover types for the YS, and they potentially provide a basis for the sustainable management and conservation of this important coastal zone.

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