潮间带湿地具有重要的生态和经济价值，但受到全球变化影响，发生大面积退化甚至丧失。掌握潮间带湿地的时空分布特征，对海岸带资源的科学管理具有重要意义。由于受到多云多雨天气和潮汐动态淹没的影响，单时相遥感数据难以获取完整的潮间带湿地信息。因此，本研究开发了一种基于时序遥感指数的潮间带湿地分类算法，并以福建省亚热带海岸带为例，基于Google Earth Engine（GEE）云平台，利用2017年—2019年Landsat 8时序影像数据，提取潮间带光滩、高潮滩植被和低潮滩植被3种典型湿地类型，分类结果总体精度97.47%，Kappa系数0.96。该算法有效降低了亚热带海岸带地区多云多雨天气和潮汐动态过程对光学遥感技术应用的影响。结果显示福建省潮间带湿地主要分布在河口与海湾处，且自北向南呈下降趋势，高潮滩植被集中分布在南部泉州湾、九龙江口、漳江口，闽北分布较少。将本研究结果与国内外同类数据集进行比较，显示出一定的优势。该方法为大尺度潮间带湿地的高精度智能分类提供了可能，为海岸带资源的可持续管理利用及区域的高质量发展提供数据基础。

Intertidal wetlands are the transitional zone between terrestrial and marine ecosystems, and they are of ecological and economic importance. However, intertidal wetlands are severely damaged due to natural causes (e.g., climate change and sea-level rise) and anthropogenic causes (e.g., coastal reclamation and excessive tourism development). Therefore, tracking the spatiotemporal changes of intertidal wetlands is important for scientific management and high-quality development of coastal areas. Compared with traditional surveying methods, remote sensing has better capacity in monitoring intertidal wetlands dynamically on a large scale. Acquiring complete information of intertidal wetland from a single-phase remote sensing image is difficult owing to the influences of cloudy weather and tidal periodic submergence. The problem of extracting the information of the intertidal zone under the influences of dynamic tidal submerge should be solved for the application of remote sensing in coastal areas.In this study, we proposed a combined method using the time-series remote sensing indices and the geographic characteristics in the subtropical intertidal wetland of Fujian Province, China on the basis of the GEE platform. Three main types of intertidal wetlands including high marsh, low marsh, and tidal flat were classified by the following steps. First, water and vegetation indices were utilized to extract water bodies and vegetation from every single image. Second, the water and vegetation frequencies derived from time-series images were used to distinguish the high marsh, low marsh, and tidal flat according to the tidal dynamics and vegetational phonology. Finally, the accuracy of the results was verified by the high-resolution image on Google Earth Pro and in situ data. The results were compared with similar datasets to assess the reliability and robustness of the proposed method.The overall classification accuracy was 97.47%, and the Kappa coefficient was 0.96. The verifications showed misclassifications in the transitional area. The total area of intertidal wetlands in Fujian Province was 1061.3 km², and the areas of high marsh, low marsh, and tidal flat were 18.1, 137.3, and 905.8 km², respectively. Intertidal wetlands were concentrated in estuaries and bays. The area of tidal flat decreased from north to south along the coast, but a converse trend of the area of high marsh was observed. The vegetation was mainly distributed in the southern Quanzhou Bay, Jiulongjiang Estuary, and Zhangjiang Estuary, and it was less in northern Fujian. Comparing the results of this study with similar datasets showed that our study improved classification accuracy in the Fujian Province. However, some objective factors such as mixed pixels and clouds could affect the accuracy of the classification.This research developed a method based on the GEE platform and time-series remote sensing indices to classify intertidal wetlands for overcoming the dilemma faced by single-phase remote sensing images in the intertidal zone information extraction. The results showed certain superiority compared with similar datasets during the same period. The method reduced the impact of the year-round cloudy and rainy weather in the subtropical coastal zone and tidal dynamics effectively. The present datasets will provide important basic data and technical supports for the sustainable management and utilization of coastal resources of the region.