A fully automatic and label-free Sentinel-1 SAR framework for green-tide mapping

• Object-based Sentinel-1 SAR green-tide mapping with spatiotemporal anomalies. • The high mapping accuracy was verified by multi-temporal global and local points. • label-free, fully automatic pipeline competitive with supervised classifiers. Green tides in the Yellow Sea are recurrent hazardous algal blooms whose optical monitoring is often hindered by cloud cover, while existing SAR approaches remain sensitive to sea state and look-alike targets and frequently require per-scene tuning or curated labels, limiting transferability and temporal consistency. To address this, we develop a label-free, fully automatic Sentinel-1 workflow that operationalizes three empirical signatures of green tides: spatial anomaly pre-location using local standard deviation on VV, edge-guided intensity separation via edge-balanced Otsu, and temporal anomaly screening using Z-scores with adaptive thresholding; an automatic object-level filter then removes non-algal marine targets. Implemented on Google Earth Engine at 10 m resolution, the pipeline delivers rapid processing without manual parameters. Validation shows high mapping accuracy: with a global stratified sample set, F1 equals 0.96 in 2019 and 0.97 in 2021; with a local edge validation set, F1 equals 0.94; in an all-pixel assessment over more than 2.5 billion pixels against a baseline, overall F1 equals 0.91. Qualitative comparisons likewise show fewer omissions of low-contrast filaments and fewer perforations within mats than GA-Net and UDNet. An ablation analysis clarifies the role of each module: the spatial pre-locator supplies contiguous candidates; the edge-guided intensity module sharpens boundaries and limits leakage; the temporal module suppresses transient bright seawater and consolidates persistent mats. Used jointly, the three constraints provide complementary information that yields the most stable cross-year performance and a favorable balance between precision and recall. Overall, the framework offers a simple, scalable, and operational pathway for fine-scale, all-weather monitoring and consistent multi-year assessment of green tides in the Yellow Sea.