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 significant environmental and socio-economic impacts. The AFAI algal index (Alternative Floating Algae Index) is often used with remote sensing data in order to estimate the Sargassum coverage, and more precisely the AFAI deviation, which consists of the difference between AFAI and AFAI of the Sargassum-free background. In this study, the AFAI deviation is computed using NASA’s 1 km Terra/MODIS (Moderate-Resolution Imaging Spectroradiometer) and ESA/Copernicus’s 20 m Sentinel-2/MSI (Multi Spectral Instrument) for the same sites and at the same time. Both MODIS and MSI AFAI deviations are compared to confirm the relevance of AFAI deviation technique for two very different spatial resolutions. A high coefficient of determination was found, thus confirming a satisfactory downsampling from 20 m (MSI) to 1 km (MODIS). Then, AFAI deviations are used to estimate the fractional coverage of Sargassum (noted FC). A new linear relationship between the MODIS AFAI deviation and FC is established using the dense Sargassum aggregations observed by MSI data. The AFAI deviation is proportional to FC with a factor of proportionality close to 0.08. Finally, it is shown that the factor is dependent on the Sargassum spectral reflectance, submersion or physiological state.

Since 2011, the proliferation of brown macro-algae of the genus Sargassum has considerably increased in the North Tropical Atlantic Sea, all the way from the Gulf of Guinea to the Caribbean Sea and the Gulf of Mexico. The large amount of Sargassum aggregations in that area cause major beaching events, which have a significant impact on the local economy and the environment and are starting to present a real threat to public health. In such a context, it is crucial to collect spatial and temporal data of Sargassum aggregations to understand their dynamics and predict stranding. Lately, indexes based on satellite imagery such as the Maximum Chlorophyll Index (MCI) or the Alternative Floating Algae Index (AFAI), have been developed and used to detect these Sargassum aggregations. However, their accuracy is questionable as they tend to detect various non-Sargassum features. To overcome false positive detection biases encountered by the index-thresholding methods, we developed two new deep learning models specific for Sargassum detection based on an encoder–decoder convolutional neural network (CNN). One was tuned to spectral bands from the multispectral instrument (MSI) onboard Sentinel-2 satellites and the other to the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3 satellites. This specific new approach outperformed previous generalist deep learning models, such as ErisNet, UNet, and SegNet, in the detection of Sargassum from satellite images with the same training, with an F1-score of 0.88 using MSI images, and 0.76 using OLCI images. Indeed, the proposed CNN considered neighbor pixels, unlike ErisNet, and had fewer reduction levels than UNet and SegNet, allowing filiform objects such as Sargassum aggregations to be detected. Using both spectral and spatial features, it also yielded a better detection performance compared to algal index-based techniques. The CNN method proposed here recognizes new small aggregations that were previously undetected, provides more complete structures, and has a lower false-positive detection rate.

Since 2011, massive strandings of holopelagic Sargassum have occurred on the coasts of the Caribbean and of West Africa. Although open ocean Sargassum mats are oases of biodiversity, their stranding has a number of negative ecological, economic and health consequences. To limit these impacts, Sargassum needs to be collected as quickly as possible to avoid its decomposition, which requires accurate predictions of the date, location and abundance of the strandings. Two complementary approaches have been developed for this purpose: satellite remote sensing technique, to detect Sargassum aggregations, and modeling, to forecast Sargassum displacement and growth. The objective of this review is to provide a synthesis of the current knowledge related to Sargassum monitoring in the tropical Atlantic Ocean. To better understand the issues surrounding Sargassum and its monitoring, the first two parts are devoted to an overview of the ecology of the two most prevailing holopelagic Sargassum species, to the current issues related to their strandings, to the causes of their occurrence in the tropical Atlantic Ocean and to their seasonal and interannual variabilities. The methods used to detect Sargassum from satellite images and their limitations are examined. The transport and biogeochemical models developed for seasonal forecast and stranding predictions are described along with their limitations. As both detection and modeling rely on validation data to assess their accuracy, previous works providing in situ characterization of Sargassum are also reviewed here. The last part provides recommendations to further increase knowledge on holopelagic Sargassum and improve the predictions of their strandings.

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).

Since 2011, Sargassum seaweed has spread widely outside the Sargasso Sea, causing massive strandings on the coasts of the West Indies and Mexico, causing serious economic, ecological, and health problems. This Atlantic pelagic alga has the characteristic of moving in rafts. According to in situ observations, their size and shape can vary with the wind. To better understand the effect of wind on Sargassum coverage and aggregation size, we conducted a large temporal (2019–2022) and spatial scale study in the West Indies using OLCI/Sentinel-3 satellite imagery. During this period, a database of nearly 1 million Sentinel-3 aggregations, including their geometric and wind characteristics, was established. Analysis of the size distribution showed that wind has a dual effect on disaggregation and agglomeration depending on wind speed and aggregation size: (1) low winds favor agglomeration for the smallest aggregations and disaggregation for the largest aggregations; (2) high winds favor disaggregation for all aggregation sizes. In addition, topography also plays a role in size distribution: the Caribbean arc favors agglomeration over offshore zones, and coastal areas favor disaggregation over offshore zones.