A new Sargassum drift model derived from features tracking in MODIS images

• Published in: Marine Pollution Bulletin
• Main Authors: Podlejski, Witold, Berline, Léo, Nerini, David, Doglioli, Andrea, Lett, Christophe
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier BV 2023
• Subjects: Sargassum algae; Computer vision; Regression; Tracking; Remote sensing; Drift; Collocation; Drifter; Tropical North Atlantic; Time series; North Atlantic
• Online Access: https://archimer.ifremer.fr/doc/00822/93349/100154.pdf; https://doi.org/10.1016/j.marpolbul.2023.114629; https://archimer.ifremer.fr/doc/00822/93349/

Massive Sargassum stranding events affect erratically numerous countries from the Gulf of Guinea to the Gulf of Mexico. Forecasting transport and stranding of Sargassum aggregates require progress in detection and drift modelling. Here we evaluate the role of currents and wind, i.e. windage, on Sargassum drift. Sargassum drift is computed from automatic tracking using MODIS 1 km Sargassum detection dataset, and compared to reference surface current and wind estimates from collocated drifters and altimetric products. First, we confirm the strong total wind effect of ≈3 % (≈2 % of pure windage), but also show the existence of a deflection angle of ≈10° between Sargassum drift and wind directions. Second, our results suggest reducing the role of currents on drift to 80 % of its velocity, likely because of Sargassum resistance to flow. These results should significantly improve our understanding of the drivers of Sargassum dynamics and the forecast of stranding events.