湿地动态范围的精准识别是湿地生态系统保护和恢复的基础。为了发挥遥感技术的高时效性及大规模重复观测的优势，本研究针对湿地具有的高空间异质性和高时间动态性特征，基于Google Earth Engine（GEE）遥感云平台全部可获取的年内Landsat OLI时序影像数据集，从湿地形成的发生学因素湿地水文要素出发，结合湿地发育成熟的诊断特征：湿地土壤和湿地植被，筛选用于湿地范围界定的水湿指数组合；采用图像合成法确定年内的高低水位；通过改进的模糊C均值（MFCM）算法弱化湿地背景的空间异质性，提高湿地与非湿地边界的对比度与区分性；选择最大类间方差法（OTSU）确定湿地消涨边界的自适应阈值；结合水湿指数组合方案叠加规则，进行年内时序湿地动态范围的识别，最终构建一套基于“要素-指标-阈值”体系的湿地动态范围精准识别技术。选取官厅水库、若尔盖湿地、鄱阳湖湿地三种典型内陆湿地为实验区，验证该套技术体系的适用性及精度，结果表明湿地范围的提取精度均高于94%，Kappa系数大于0.88。结果证明该方法有效提高了湿地时空动态范围识别的精度和效率，以期为长时序、大范围湿地动态监测与制图提供有效的技术支撑。

Accurate identification of the wetland dynamic range is the basis for the protection, restoration of the wetland ecosystem. In order to give full play to the advantages of high timeliness and large-scale repeated observation of remote sensing technology, and considering the characteristics of high spatial heterogeneity and high temporal dynamics of wetlands, all Landsat OLI time series image data sets available for Google Earth Engine (GEE) were used to study accurate identification of wetland dynamic range under water level fluctuation. Three typical inland wetlands were selected as research areas, based on the genetic factors and the hydrological factors of wetland, combined with the diagnostic characteristics of mature wetland: hygrophyte and wet soil, the combination of water-wetness indices for defining wetland scope was selected. Image composition was used to determine the high and low water levels in one year. Modified fuzzy C-means (MFCM) algorithm was proposed to reduce the spatial heterogeneity of wetland background and improve the contrast and distinction between wetland and non-wetland boundaries. The Maximum Between-Class Variance (OTSU) was selected to determine the adaptive threshold of wetland disinflation boundary, and combined with the superposition rules of the water-wetness index dynamic combination scheme to identify the wetland within one year.. Finally, a set of accurate identification technology of wetland dynamic range based on the " elements-index-threshold" system was constructed. Typical inland wetlands such as Guanting Reservoir, Zoige wetland and Poyang Lake wetland were selected as experimental areas to verify the applicability and accuracy of the set of technology system. The results showed that the extraction accuracy of wetland range was higher than 94%, and the Kappa coefficient was greater than 0.88. This research improve the accuracy and efficiency of wetland spatio-temporal dynamic range identification, hoping to provide effective support for long-term and large-scale wetland dynamic monitoring and mapping.