Oral Session 3 | Tuesday, October 4, 09:00–09:20 | Abstract 478

Estimation of the Sargassum fractional coverage and immersion depth using OLCI/Sentinel-3 data in the Caribbean Sea (North Atlantic Ocean)

The Sargassum is an invasive species of algae that aggregates and drifts in the open ocean. Since the last decade, Sargassum is observed in unusually high quantities from the Caribbean Sea to Brazil up to the coast of Northwest Africa. Previous remote sensing works proposed an index called the Maximum Chlorophyll Index (MCI) based on the red and near infrared spectral bands of OLCI/Sentinel-3 sensor to identify Sargassum occurrences. In the Caribbean Sea, rough waters often drown the aggregations to more than 1 meter deep. The amplitude of the Sargassum water leaving reflectance could then be too weak to be properly detected using MCI-like index. In this study, the forward semi-analytical radiative transfer model previously proposed by Lee et al. (Appl. Opt., 1999) was adapted to simulate the sea surface reflectance by accounting for the occurrence of Sargassum aggregations at a given depth. The model is then inverted using an optimization approach to derive both the immersion depth and the Sargassum coverage from the OLCI surface reflectance data. The inversion of the model shows that the retrieved Sargassum coverage per pixels ranges from 0.5% to 21.5% over the study area. The consideration of the immersion depth of Sargassum in the retrieval algorithm as proposed here implies a relative increase of the scene coverage values from 21.5% to 85.3% in comparison to the current MCI method. This study reveals that the immersion depth of Sargassum should be considered for more realistic estimates of Sargassum coverage in the open ocean.

*Lea Schamberger, Universite de Toulon

Audrey Minghelli, Laboratoire d’Informatique et Système (LIS), Université de Toulon

Malik Chami, Laboratoire Atmosphères Milieux Observations Spatiales (LATMOS), Sorbonne Université

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