海上风电场被沿海国家广泛采用，以获得清洁、绿色的能源；它们对环境的影响越来越受到关注。尽管海上风电场数据集可通过能源行业在商业上获得，但单个风力涡轮机的确切空间分布及其建造轨迹的记录相当不完整，尤其是在全球范围内。本研究利用从2015 年到 2019 年的 Sentinel-1 合成孔径雷达 (SAR) 时间序列图像构建了一个基于全球遥感的海上风力涡轮机 (OWT) 数据库，开发了基于百分比的年度 SAR 图像收集减少和自适应谷歌地球引擎平台中的阈值算法来识别全球 OWT 的时空分布。到 2019 年，共有 6,924 台风力涡轮机在14个沿海国家被建造。本研究进行了算法性能分析和验证，使用独立的验证数据集提取精度超过99%。该数据集可以进一步了解 OWT 的环境影响，并支持有效的海洋空间规划以促进可持续发展。

Offshore wind farms are widely adopted by coastal countries to obtain clean and green energy; their environmental impact has gained an increasing amount of attention. Although offshore wind farm datasets are commercially available via energy industries, records of the exact spatial distribution of individual wind turbines and their construction trajectories are rather incomplete, especially at the global level. Here, we construct a global remote sensing-based offshore wind turbine (OWT) database derived from Sentinel-1 synthetic aperture radar (SAR) time-series images from 2015 to 2019. We developed a percentile-based yearly SAR image collection reduction and autoadaptive threshold algorithm in the Google Earth Engine platform to identify the spatiotemporal distribution of global OWTs. By 2019, 6,924 wind turbines were constructed in 14 coastal nations. An algorithm performance analysis and validation were performed, and the extraction accuracies exceeded 99% using an independent validation dataset. This dataset could further our understanding of the environmental impact of OWTs and support effective marine spatial planning for sustainable development.