Accurate wetland mapping is essential for ecosystem monitoring, yet dense pixel-level annotation is prohibitively expensive and practical applications usually rely on sparse point labels, under which existing deep learning models perform poorly, while strong seasonal and inter-annual wetland dynamics further render single-date imagery inadequate and lead to significant mapping errors; although foundation models such as SAM show promising generalization from point prompts, they are inherently designed for static images and fail to model temporal information, resulting in fragmented masks in heterogeneous wetlands. To overcome these limitations, we propose WetSAM, a SAM-based framework that integrates satellite image time series for wetland mapping from sparse point supervision through a dual-branch design, where a temporally prompted branch extends SAM with hierarchical adapters and dynamic temporal aggregation to disentangle wetland characteristics from phenological variability, and a spatial branch employs a temporally constrained region-growing strategy to generate reliable dense pseudo-labels, while a bidirectional consistency regularization jointly optimizes both branches. Extensive experiments across eight global regions of approximately 5,000 km2 each demonstrate that WetSAM substantially outperforms state-of-the-art methods, achieving an average F1-score of 85.58%, and delivering accurate and structurally consistent wetland segmentation with minimal labeling effort, highlighting its strong generalization capability and potential for scalable, low-cost, high-resolution wetland mapping.

翻译：精确的湿地制图对生态系统监测至关重要，然而密集的像素级标注成本极高，实际应用通常依赖稀疏的点标注，在此条件下现有深度学习模型表现不佳；同时强烈的季节性与年际湿地动态变化使得单时相影像不足，并导致显著的制图误差。尽管基础模型如SAM在点提示下展现出良好的泛化能力，但其本质是为静态图像设计，无法建模时序信息，导致在异质性湿地中产生破碎的分割掩码。为克服这些限制，我们提出WetSAM，一种基于SAM的框架，通过双分支设计集成卫星图像时间序列以实现基于稀疏点监督的湿地制图：时序提示分支通过层级适配器与动态时序聚合扩展SAM，以从物候变化中解耦湿地特征；空间分支采用时序约束的区域生长策略生成可靠的密集伪标签；同时双向一致性正则化联合优化两个分支。在八个全球区域（各约5000平方公里）上的大量实验表明，WetSAM显著优于现有先进方法，平均F1分数达到85.58%，能够以极低的标注成本实现精确且结构一致的湿地分割，凸显了其强大的泛化能力以及在可扩展、低成本、高分辨率湿地制图方面的潜力。