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SARGASSUM SHAPE ATTRIBUTES FOR KNOWLEDGE AND SATELLITE DETECTION IMPROVEMENT
Sargassum has different shapes resulting from several conditions, from concentration or aggregation to currents and wind. On the other hand, detection has been mainly based on algal floating indexes that may have issues regarding false positives that confuse mainly clouds, cloud shadows, and sun glint. Here, we analyzed the shape and spatial configuration metrics of sargassum polygons to improve the confidence of sargassum detection, minimize false positives in final outputs, and generate knowledge about sargassum aggregation patterns. We implemented this approach by contrasting 38 metrics in sargassum polygons obtained with medium-resolution satellite sensor Landsat-OLI, and we evaluated the confusion with other elements in the images. We also valued the contribution of several metrics to improve pelagic sargassum satellite detection. Twelve spatial and shape metrics were statistically different for sargassum polygons regarding false positive polygons, and we proved that these metrics improve the final detection process. This approach could be used as inputs for spatiotemporal pattern analysis, forecasting models, and impact analysis, among others, due to better confidence in the sargassum detection, avoiding over-estimations that can drive false conclusions due to the extreme presence of false positives.