我国南黄海地区夏季常发生浒苔灾害，引发严重的生态环境和经济问题，精准获取浒苔时空分布信息对灾害的定量评估和制定相应的防控策略具有重要意义。光学遥感影像可用于区域尺度浒苔空间信息精准提取，但常受限于数据的空间分辨率和云雨影响，难支撑定期、稳定的浒苔监测。合成孔径雷达影像具有全天候全天时定期观测的优势，为浒苔动态监测与时空分布研究提供了更多可能，但目前研究尚不充分。在Sentinel-1影像支持下，本文提出了一种顾及后向散射系数和标准差特征的浒苔自动提取方案。该方法首先通过自适应阈值方法对影像进行分割，根据边缘浒苔与海水在后向散射系数标准差之间的差异，剔除后向散射系数较高的海水，获得浒苔初提取结果。最后针对海上与浒苔特征相似的目标，依据不同类型分别设计了基于时序信息和后向散射系数阈值的后处理方案。基于Google Earth Engine平台，以2021年南黄海为例，实现了2021年5-7月南黄海区域的浒苔动态监测。结果表明，提出的浒苔提取方法精度达93%，2021年南黄海区域观测到的浒苔最大覆盖面积达1700多平方公里，在浒苔发生发展过程中呈现“分散发育、聚集爆发、扩散消亡”的整体趋势。

The recurring Ulva prolifera disasters in the South Yellow Sea region of China during the summer have a significant impact on the environment, ecology, and economy. To address this issue, it is crucial to obtain accurate spatial and temporal distribution information of Ulva prolifera for quantitative assessment of the disaster and development of effective prevention and control strategies. Optical remote sensing images provide spatial information on Ulva prolifera at a regional scale. However, they are limited by low spatial resolution and the influence of clouds and rain, making regular and stable monitoring of Ulva prolifera challenging. In contrast, synthetic aperture radar (SAR) images provide all-weather, all-day observation, which enables more possibilities for dynamic monitoring and research on the spatial and temporal distribution of Ulva prolifera. To this end, this paper proposes an automatic extraction process for Ulva prolifera using the backscattering coefficient and standard deviation features, supported by Sentinel-1 images. The method involves an adaptive thresholding approach to segment the image. The seawater with higher backscattering coefficient is excluded to obtain the initial extraction result of Ulva prolifera, based on the difference between edge Ulva prolifera and seawater in the standard deviation of backscattering coefficient. The post-processing schemes are designed based on temporal information and backscattering coefficient thresholds for targets with similar characteristics to Ulva prolifera in the sea according to different types. The proposed method was employed to monitor the dynamic distribution of Ulva prolifera in the South Yellow Sea region from May to July 2021, using the Google Earth Engine platform. The results show that the accuracy of the extraction method reaches 93%, and the maximum coverage of Ulva prolifera observed in the South Yellow Sea region in 2021 was over 1700 square kilometers. The analysis reveals an overall trend of "scattered development, aggregation outbreak, and diffusion extinction" in the process of Ulva prolifera development.