## 5 Discussion
The proportion of coverage estimated by the neural network between 2 m and 5 m ranges from 30% to 51%. The scene acquired on 2 May 2021, shows a relative coverage difference lower than 1% when using the neural network approach relatively to the \(\delta\)MCI approach (Table 3). Such a value can appear inconsistent relatively to the high percentage of estimation of immersed _Sargassum_ (42% deeper than 2 m). However, it should be highlighted that the scene is highly impacted by clouds (about 60% of cloud coverage) which could lead to an overestimation of _Sargassum_ FC by the \(\delta\)MCI approach. This is because the high coverage of clouds could prevent the correction of the median filtering window (Section 2.3.4). The \(\delta\)MCI approach detects more _Sargassum_ occurrences in the east part of the scene. For such an area, the neural network approach does not detect the occurrence of _Sargassum_, which could explain the weak value of the relative coverage difference (<1%). However, it remains difficult to draw any conclusions about the best method that could estimate _Sargassum_ occurrence as far as the acquisition of in situ data is lacking to validate the retrieved products. The IFREMER CERSAT Global Blended Mean Wind Fields is a database that is available from the Copernicus services CMEMS, which provides 6-hourly wind components including wind speed between 1992 and 2020 [38]. The spatial means of the wind provided for the area of interest are 5.5 m s\({}^{-1}\) for 14 September 2020, and also for 28 December 2020; 6.5 m s\({}^{-1}\) for 8 July 2017; and more than 7.2 m s\({}^{-1}\) for 14 June 2020, and 27 May 2018. The scenes for which the wind speed is the highest (>6.5 m.s\({}^{-1}\)) corresponds to the scenes where a higher coverage is estimated between 2 m and 5 m (>39% here). The wind speed  
Figure 13: (**a**) Spatial distribution of the _Sargassum_ detections for the OLCI scene acquired on 8 July 2017, using the FC values retrieved by the neural network approach (blue), by the \(\delta\)MCI approach (yellow) and by both approaches (green); (**b**) RBG composite image of a regional area in the north-east corner of the OLCI scene. could then be potentially linked to the abundance of immersed _Sargassum_[29]. Previous observations also reported a relationship between the wind and the vertical mixing of _Sargassum_; the presence of _Sargassum_ below the surface was particularly observed for strong wind conditions (i.e., >4 m s\({}^{-1}\)) [19]. According to the calibration constant of 3.34 kg m\({}^{-2}\) proposed by [PERSON] et al. [15], to convert the area of Sargassum (km\({}^{2}\)) into total wet biomass (metric tons), the Sargassum coverage (km\({}^{2}\)) that are outlined in Table 3 for the \(\delta\)MCI and the neural network approaches can be converted into Sargassum biomass (Million tons) for the various OLCI scenes (Table 4). It is interesting to note that the derived Sargassum biomass values roughly show the same order of magnitude as the biomass estimates performed by [PERSON] et al. [15] in the Caribbean Sea and the central-west Atlantic region for July 2015 (4.4 million tons) and by [PERSON] [39] for the Sargasso Sea (7-10 million tons), bearing in mind that the Sargassum area coverage differ between their satellite dataset and the dataset used in this study.