## 1 Introduction  
The _Sargassum_ is a species of algae originally found in the \"Sargasso Sea\", which is located in the northwestern region of the tropical Atlantic Ocean offshore the Florida coastlines (USA). In the last decade, _Sargassum_ has be observed in large amounts from the Caribbean Sea to Brazil and could extend up to the coastlines of the north-west of Africa [1]. Such a shift in the _Sargassum_ spatial distribution, which was observed in 2011 by [PERSON] et al. [2], can be ascribed to both global change, particularly the water warming, and to the nutrient supply caused by farming [3]. The _Sargassum_ are transported by the currents and winds until they wash up on shores. Two pelagic species of _Sargassum_, namely _S. natans_ and _S. flutians_, are prevailing in beaching events. Those species have a key role in the ecosystem by providing habitats and by sequestrating carbon [4, 5]. However, the decomposition of such algae generates Hydrogen Sulfide (H\({}_{2}\)S) gas which releases a \"rotten egg\" odor and can impact inhabitants' health. The strong increase in the beaching events also impacts the tourism and the coastal environment [1]. The biological properties of _Sargassum_ have been recently investigated to better understand their formation, aggregation and life cycle [6; 7]. Satellite remote sensing observations are relevant assets to provide a wide spatial and temporal extent of the _Sargassum_ distribution at synoptic scale. The first spectral index, namely the Maximum Chlorophyll Index (MCI), was proposed by [PERSON] et al. to detect intense plankton bloom [8] and _Sargassum_ aggregations [9; 10] using MERIS data. This index is based on a linear combination of three spectral bands of MERIS sensor in the near infrared (NIR) domain. This is because the _Sargassum_ reflectance shows a strong increase in the red-edge and near NIR parts of the spectrum similarly as it could be observed for land vegetation spectral features (see Figure 2 in [10]). The water leaving reflectance spectrum is then characterized by a pronounced signature in those spectral domains for _Sargassum_ dominated waters. The identification (or detection) of _Sargassum_ from remotely sensed data acquired over the open ocean currently relies on such a spectral signature. MCI was further adjusted to be applied to the satellite sensor \"Ocean Land Color Imager (OLCI)\" onboard Sentinel-3 [11]. [PERSON] proposed another spectral index, the Floating Algae Index (FAI) [12], using MODIS sensor to detect _Sargassum_. One limitation of FAI index is that it could also be an indicator of the presence of clouds, particularly for high FAI values, which can make it challenging to quantify the actual coverage of _Sargassum_ in the open ocean. [PERSON] and [PERSON] [13] improved the FAI index by proposing an Alternative Floating Algae Index (AFAI), which is supposed to be less sensitive to the presence of clouds. They were able to estimate the abundance of _Sargassum_ in the Gulf of Mexico. [PERSON] and [PERSON] [13] developed an approach to determine the coverage of _Sargassum_ per pixels (i.e., their abundance) based on a linear relationship between the fractional coverage (FC) and the AFAI index [14]. FC is defined as the ratio (in %) between the surface area of _Sargassum_ aggregation within one pixel and the total surface area of that pixel. The biomass quantity of Sargassum within a pixel can be estimated using a relationship defined empirically by [15] between the fractional coverage, the pixel size resolution and a calibration constant which value is 3.34 kg m\({}^{-2}\). The correct estimation of the fractional coverage from remote sensing data is then crucial to determine the amount of Sargassum biomass in the ocean and to improve the strandings forecast of Sargassum in coastal zones. Recently, neural network approaches were implemented to detect the presence of _Sargassum_ in the open ocean. [16].