Since 2011, massive strandings of holopelagic Sargassum have occurred on the coasts of the Caribbean and of West Africa. Although open ocean Sargassum mats are oases of biodiversity, their stranding has a number of negative ecological, economic and health consequences. To limit these impacts, Sargassum needs to be collected as quickly as possible to avoid its decomposition, which requires accurate predictions of the date, location and abundance of the strandings. Two complementary approaches have been developed for this purpose: satellite remote sensing technique, to detect Sargassum aggregations, and modeling, to forecast Sargassum displacement and growth. The objective of this review is to provide a synthesis of the current knowledge related to Sargassum monitoring in the tropical Atlantic Ocean. To better understand the issues surrounding Sargassum and its monitoring, the first two parts are devoted to an overview of the ecology of the two most prevailing holopelagic Sargassum species, to the current issues related to their strandings, to the causes of their occurrence in the tropical Atlantic Ocean and to their seasonal and interannual variabilities. The methods used to detect Sargassum from satellite images and their limitations are examined. The transport and biogeochemical models developed for seasonal forecast and stranding predictions are described along with their limitations. As both detection and modeling rely on validation data to assess their accuracy, previous works providing in situ characterization of Sargassum are also reviewed here. The last part provides recommendations to further increase knowledge on holopelagic Sargassum and improve the predictions of their strandings.

Since 2011, massive strandings of pelagic Sargassum have become a recurrent environmental hazard across the tropical Atlantic and Caribbean archipelago, creating an urgent need for reliable short-term drift forecasts to support coastal risk management. This study evaluates key sources of uncertainty in operational Sargassum drift forecasting by analyzing the sensitivity of Lagrangian simulations to the representation of floating material and to environmental forcing fields. The analysis uses two complementary observational datasets: trajectories of four GPS-tracked Sargassum mats deployed near Puerto Rico and thirteen 24 h displacement vectors derived from sequential Sentinel-3 satellite detections across the tropical North Atlantic. Drift simulations were performed with the MOTHY model under multiple configurations, testing two material parameterizations, different atmospheric forcings, and several ocean circulation products and vertical current integration strategies. The results indicate that the best agreement with observed trajectories is obtained for partially immersed structures, highlighting the importance of balancing wind exposure and hydrodynamic drag. Sensitivity experiments further show that ocean circulation forcing dominates trajectory skill, while higher-resolution atmospheric forcing provides limited improvement under offshore conditions. Overall, the study confirms the importance of accurately representing upper-ocean transport processes and provides observational support for several operational choices implemented in the Météo-France Sargassum forecasting system.

This datapaper presents the methodology and the associated dataset of Sargassum detection from Sentinel-3 OLCI images developed at Météo-France. The dataset includes daily detections over the whole tropical Atlantic Ocean, the Caribbean Sea and the Gulf of Mexico, from May 2018 up to date. It is updated daily with the latest detections. Sargassum is detected through a physically-based approach relying on two indices (the AFAI and the MCI) and using adaptive thresholds. A comparison of this dataset with two existing ones reveals a reduction in masked areas and improved detection of Sargassum, particularly in areas of sunglint and at the edges of clouds. The entire dataset is freely available on the Odatis platform and can be downloaded in NetCDF format (DOI: 10.12770/1eb82d09-77ed-4f63-9f03-2c3516a9713d; Météo-France, 2026). Two variables are available: the status of detection and the deviation of the AFAI for pixels including Sargassum.