Since 2011, unprecedent massive landings of sargassum seaweed (Sargassum fluitans and Sargassum natans) have been observed along the shorelines of a huge area encompassing the Gulf of Mexico, the Caribbean Sea and West Africa, having tremendous negative impacts over local communities.

Satellite imagery allows to detect the presence of floating sargassum and is a key tool to help scientists to understand the origin and the seasonality of the sargassum movements in the Atlantic, and to support local communities in the management of the next sargassum influxes.

The basic processing of the observations from the Advanced Baseline Imager (ABI) sensor onboard the GOES16 geostationary satellite is performed by the new software designed by Hygeos and operated by CLS since March 2022. The chief advantage of this geostationary satellite is the capability to acquire one image every 10 min over a visibility circle centered on the 75W meridian, thus covering the Atlantic ocean from the Gulf of Mexico to the offshore of Western Africa. This capability offers a much greater probability to measure the ocean surface within one day, which is not the case for sun-synchronous satellites. The daily spatial coverage improves by a factor of 2 to 3 thanks to the daily movement of the clouds, compared to a single slot. The multi-temporal observation also provides the ability to monitor the evolution of the sargassum position. The drawback is the much-reduced radiometric sensitivity of the sensor, together with the coarser pixel resolution (1 km), compared to the ocean colour sensors typically used for sargassum monitoring (OLCI on Sentinel-3A & 3B, MODIS or Sentinel-2). The sargassum index used by Hygeos is the FAI anomaly, which is the difference between the pixel FAI (Hu, 2009) and a background FAI value of sargassum-free FAI. This index also includes several steps to enhance the data quality: radiometric correction, masking and denoising.

References:

- Hu, C., 2009 : A novel ocean color index to detect floating algae in the global oceans. Rem. Sensing of Environment 113, 2118-2129. DOI : https://doi.org/10.1016/j.rse.2009.05.012