SuperDove satellite detection of matter-loaded windrows with a focus on extreme environmental events

Matter-loaded windrows are high-density submesoscale accumulation structures that form on the ocean surface and are often associated with extreme environmental events. This study presents a framework for windrow detection using a UNet deep-learning model applied to Planet SuperDove (PSD) 8-band satellite imagery (FLODAS). The model is trained on hand-annotated cases representing a variety of windrows and evaluated using complementary performance metrics, demonstrating consistent detection across independent scenes, while also revealing scene-specific patterns of false positives. Spectral analysis and classification based on the spectral angle distance metric is used to examine the potential for discriminating floating matter types using the PSD bands. Limited ground-truth data and the incomplete spectral characterisation of floating matter currently constrain classification performance. Application of the framework in the Aegean Sea enabled the identification of surface matter accumulations linked to storm-driven runoff and riverine discharge. The results demonstrate the potential of the framework to support in situ sampling and debris-removal operations, as well as short-term assessments of matter transport and windrow dynamics. Overall, the proposed framework represents a proof-of-concept step towards the development of operational detection systems for surface matter and marine debris accumulations.