SeSaM contributes to sargassum algae management in all the territories affected by sargassum strandings. Using satellite observations and numerical modelling, SeSaM allows observing the situation in real time and forecasting the next seasonal sargassum proliferation and influxes.

Seasonal Sargassum Monitoring


Before 2011, pelagic Sargassum spp. bloomed preferentially in the Sargasso Sea (North Atlantic Ocean area) and in the northwestern tropical Atlantic. They are now found in large quantities on the coasts of the Lesser Antilles, Central America, Brazil and West Africa. Satellite imagery reveals that these strandings come from colossal quantities of algae drifting from the central tropical Atlantic between 0 and 10°N (Wang and Hu 2016, Langin 2018). Recent hypothesis explains this change in the sargassum Atlantic dynamics by a combination of causes from climate change and anthropic factors. It finds its origin with a strong North Atlantic Oscillation anomaly, which has occurred during the winter 2009-2010 causing the sargassum to enter the Northern Equatorial Atlantic Circulation (Johns et al. 2020).

Observing, modeling and forecasting the Sargassum proliferation and strandings are essential for designing effective integrated risk management strategies and is a strong and pressing demand from the civil society.

The aim of the SeSAM project is to contribute to the existing international initiatives on this topic (NOAA, GEO blue Planet). It leverages the existing large-scale Sargassum detection operational system developed by CLS (SAMTool), and the innovative growth/transport Sargassum model developed by IRD (ANR FORESEA project). It also relies on the operational capacity and framework of Mercator Ocean International to implement a robust Digital Twin service based on these assets.

SeSAM will provide access to the satellite detection products to monitor the evolution sargassum in near real time, and to the seasonal forecast produced at basin scale to help with preparation and mitigation plans in advance.


Satellite observations of sargassum

Since 2018, CLS has been developing and operating a monitoring system of the Sargassum situation in real time, based on several satellite sensors. The system produces daily maps of sargassum presence index at 300m resolution. Maps at 20m resolution are also produced every two/three days in coastal areas. These satellite observations will cover the full tropical Atlantic (5°S-30°N; 10°E-100°W) and will include reprocessed time series which are critical for the seasonal forecast application.

Detection sargasses

Figure 1. Sargassum detection at 300-m resolution from OLCI instrument on Sentinel-3 for 15th June 2019. © CLS

Seasonal forecasting of sargassum proliferation and influxes

On the modelling side, the ANR project FORESEA (PI J. Jouanno; 2019-2022), funded by the AAP Sargassum in 2019, laid the foundation for the development basin scale sargassum forecasting system. From January 2022, a seasonal forecast has been operated in “best effort mode”. It includes a mechanistic modeling of the Sargassum populations (referred to as NEMO-Sarg1.0 model; Jouanno et al. 2021b) that implement the biology and complex drift properties of the Sargassum rafts and integrates the previsibility of the regional climate system (atmosphere and ocean dynamics).

Prevision sargasses

Figure 2. Illustration of the forecasts and ability of the system developed by IRD to predict the large scale distribution of Sargassum with 1 to 7 months in advance. On the left: monthly observations of Sargassum areal coverage (%) obtained from MODIS in January, April, and July 2021. On the right: forecast of Sargassum coverage (%) initialized in January 1st 2021 and shown as monthly averages for 0.5 month lead (January 2021), 3.5-month lead (April 2021) and 6.5-month lead (July 2021) forecasts. The forecast is an average of an ensemble of 25 simulations, explaining why the simulated fields are much smoother than the observed field. © IRD

Application site(s)

Sargassum algae stranding happen on most shorelines of the Tropical Atlantic Ocean, from the Caribbean Sea to Western Africa. The SeSaM project will deliver satellite observation and model forecast to all countries and territories in this region, with a focus on communities with Sargassum management and mitigation plans.



  • OLCI Sentinel-3
  • MODIS Aqua
  • MSI Sentinel-2
  • OLI Landsat-8 & 9


SEAS5 seasonal forecast from ECMWF (European Centre for Medium-Range Weather Forecasts)

Results - Final products

Products will be hosted by AVISO / ODATIS and on a dedicated website. The outputs of the SeSaM project will be open and free, and shared with international community hubs (e.g. that is co-organized by GEO Blue Planet, IOCARIBE of IOC-UNESCO and others).

Prevision sargasses juil21

Figure 3. Prévision de la couverture surfacique en Sargassum (%) pour juillet 2021 initialisée au 1er février 2021 (6 mois d'avance). La prévision s'appuie sur le modèle de Sargassum NEMO-Sarg, qui intègre à la fois le transport et les connaissances actuelles sur la physiologie des Sargassum. © IRD


  • Jouanno, J., Morvan G., Berline, L., Benshila R., Aumont, O., Sheinbaum, J., Ménard, F (2023).  Prévision saisonnière habile de la prolifération des Sargassum dans l'Atlantique Tropical. En révision GRL.

  • Jouanno, J., Benshila, R., Berline, L., Soulié, A., Radenac, M.-H., Morvan, G., Diaz, F., Sheinbaum, J., Chevalier, C., Thibaut, T., Changeux, T., Menard, F., Berthet, S., Aumont, O., Ethé, C., Nabat, P., et Mallet, M. : A NEMO-based model of Sargassum distribution in the Tropical Atlantic : description of the model and sensitivity analysis (NEMO-Sarg1.0), Geoscientific Model Development, 14(6), 4069-4086.

Related projects

  • ANR project FORESEA (Forecasting seasonal Sargassum Events in the Atlantic, PI J. Jouanno ; 2019-2023),

  • CLS, SamTOOL system

  • TOSCA SARGAT (Pelagic Sargassum aggregations and transport ; PI J. Jouanno ; 2023-2025)

  • SODA project (Sargassum Operational Detection Algorithms) from Copernicus Marine Service

  • Digital Twin Ocean from Mercator Ocean


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