Synergy between Low Earth Orbit (LEO)—MODIS and Geostationary Earth Orbit (GEO)—GOES Sensors for Sargassum Monitoring in the Atlantic Ocean

Jacques Descloitres,Audrey Minghelli,Léo Berline,Malik Chami,Cristele Chevalier,Philippe Blanc

doi:10.3390/rs13081444

Abstract

Since 2011, massive stranding of the brown algae Sargassum has regularly affected the coastal waters of the West Caribbean, Brazil, and West Africa, leading to heavy environmental and socio-economic impacts. Ocean color remote sensing observations as performed by sun-synchronous satellite sensors such as MODIS (NASA), MERIS (ESA), or OLCI (ESA/Copernicus) are used to provide quantitative assessments of Sargassum coverage through the calculation of indices as the Alternative Floating Algae Index (AFAI). Sun-synchronous sensors usually provide at best one daytime observation per day of a given oceanic area. However, such a daily temporal revisit rate is not fully satisfactory to monitor the dynamics of Sargassum aggregation due to their potentially significant drift over the course of the day as a result of oceanic currents and sea surface wind stress. In addition, the sun glint and the presence of clouds limit the use of low earth orbit observations, especially in tropical zones. The high frequency sampling provided by geostationary sensors can be a relevant alternative approach in synergy with ocean color sun-synchronous sensors to increase the temporal resolution of the observations, thus allowing efficient monitoring of Sargassum dynamics. In this study, data acquired by a geostationary satellite sensor located at 36,000 km from Earth, namely GOES-16 (NASA/NOAA), which was primarily designed for meteorology applications, are analyzed to investigate the Sargassum dynamics. The results demonstrate that a GOES-16 hourly composite product is appropriate to identify Sargassum aggregations using an index commonly used for vegetation monitoring, namely NDVI (Normalized Difference Vegetation Index). It is also shown that GOES hourly observations can significantly improve the simulated drift obtained with a transport circulation model, which uses geostrophic current, wind, and waves. This study thus highlights the significant relevance of the effective synergy between sun-synchronous and geostationary satellite sensors for characterizing the Sargassum dynamics. Such a synergy could be summarized as follows: (i) A sun-synchronous sensor enables accurate Sargassum detection and quantitative estimates (e.g., fractional coverage) through AFAI Level-2 products while (ii) a geostationary sensor enables the determination of the displacement features of Sargassum aggregations (velocity, direction).

Highlights

Unprecedented massive stranding of the brown macro-algae Sargassum fluitans andS. natans has been observed along the coastline of French Guyana, the Antilles Islands, and the Caribbean Sea over the last decade
A spectral index called Maximum Chlorophyll Index (MCI) was first defined by Gower et al [1] for the Medium Resolution Imaging Spectrometer (MERIS) satellite sensor (300 m resolution). Such an index relies on the spectral peak of the water leaving radiance at 709 nm that is mostly induced by the Sargassum algae optical signature
The δAFAI product derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data on 8 August 2018 at 14:35 UTC

Summary

Introduction

S. natans (hereinafter Sargassum) has been observed along the coastline of French Guyana, the Antilles Islands, and the Caribbean Sea over the last decade These pelagic algae float in the upper surface layer in offshore waters and can cluster into large aggregations that are transported by currents over long distances across the Atlantic Ocean. A spectral index called Maximum Chlorophyll Index (MCI) was first defined by Gower et al [1] for the Medium Resolution Imaging Spectrometer (MERIS) satellite sensor (300 m resolution). Such an index relies on the spectral peak of the water leaving radiance at 709 nm that is mostly induced by the Sargassum algae optical signature. The MCI index was previously used to study the spatial distribution of Sargassum in the Gulf of Mexico and in the western

Methods

Results

Conclusion