发表机构
Pacific Northwest National Laboratory; NASA Goddard Space Flight Center; Fujitsu Research of America; University of Wisconsin–Madison（太平洋西北国家实验室; 美国国家航空航天局戈达德太空飞行中心; 富士通美国研究院; 威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
利用Sentinel-1 SAR数据和AlphaEarth嵌入，以动态世界水体类别为弱监督，实现无云条件下全球地表水测绘，显著提升交并比至0.85。
AI 中文摘要
动态世界（Dynamic World, DW）利用 Sentinel-2（S2）影像以10米分辨率在全球范围内绘制土地利用和土地覆盖，但仅限于无云观测，这限制了地表水可被测绘的地点和时间。我们将DW的水体类别用作Sentinel-1（S1）合成孔径雷达（SAR）模型的弱监督信号，以便为每次S1采集生成类似DW的水体地图。谷歌的AlphaEarth Foundations（AEF）年度嵌入提供空间背景，而S1后向散射提供采集时的观测信息。在53个全球分布的场景中，这些场景具有在S1过境48小时内获取的3米PlanetScope影像的独立标注，仅使用S1的模型已达到0.77的池化水体交并比（IoU），与运行中的OPERA DSWx-S1产品的0.75相当，而加入AEF后将其提升至0.85。融合模型在53个场景中的44个上优于仅使用S1的模型，并在48个场景上超过OPERA；在独立的S1S2-Water基准上，它达到0.94，而OPERA为0.87。因此，光学土地覆盖产品可以为SAR地表水测绘提供可扩展的训练标签。
英文摘要
Dynamic World (DW) maps land use and land cover globally at 10 m from Sentinel-2 (S2) imagery, but only for cloud-free observations, which limits where and when surface water can be mapped. We use the DW water class as weak supervision for a Sentinel-1 (S1) synthetic aperture radar (SAR) model so that DW-like water maps can be produced for every S1 acquisition. Google's AlphaEarth Foundations (AEF) annual embedding supplies spatial context, while S1 backscatter supplies the acquisition-time observation. On 53 globally distributed scenes with independent annotations of 3 m PlanetScope imagery acquired within 48 h of the S1 overpass, the S1-only model already reaches a pooled water intersection over union (IoU) of 0.77, comparable to 0.75 for the operational OPERA DSWx-S1 product, and adding AEF raises it to 0.85. The fused model improves on the S1-only model on 44 of 53 scenes and exceeds OPERA on 48, and on the independent S1S2-Water benchmark it reaches 0.94, compared with 0.87 for OPERA. Optical land-cover products can thus provide scalable training labels for SAR surface water mapping.
Comments8 pages, 3 figures, 5 tables; includes 3 pages of supplementary material
