Abstract. 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.
Received: 23 Jul 2026 – Discussion started: 24 Aug 2026
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This article presents a dataset of Sargassum detections and the methodology used to obtain it. The detections were made using images from the OLCI sensor aboard the Sentinel-3 satellite. They are freely available for the entire tropical Atlantic, the Caribbean Sea, and the Gulf of Mexico, covering the period from May 2018 to the present, with the latest detections added daily. These detections are intended for use in both operational and research applications.
This article presents a dataset of Sargassum detections and the methodology used to obtain it....
