## 5 Discussion  
The retrieval of FC can also be carried out using the \(\delta\)MCI approach (Figure 13a). A comparison of the different approaches (\(\delta\)MCI and neural network) is also performed on the resulting OLCI image. It is observed that the neural network successfully detects most of the aggregations that were also identified from \(\delta\)MCI. However, the \(\delta\)MCI approach detects more _Sargassum_ occurrences than the neural network approach in the north-east part of the scene. It may be caused by very thin clouds in this area, which could lead to an overestimation of the _Sargassum_ occurrence by \(\delta\)MCI. A composite RGB image (Figure 13b) which focuses on the north-east part of the scene enables visualization of the thin clouds. The red circle in Figure 13a shows a _Sargassum_ detection made by the \(\delta\)MCI approach that is likely to be a thin cloud based on Figure 13b (see red circle). More _Sargassum_ contaminated pixels are retrieved in the center of the scene using the neural network when comparing to the \(\delta\)MCI approach. Even though the lack of in situ data prevents the validation of the additional _Sargassum_ detections provided by the neural network approach, it should be highlighted that those additional occurrences are located in the vicinity of the main aggregation patterns that are already detected by the \(\delta\)MCI. The latter point corroborates the fact that the neural network retrievals are consistent with the current standard method. The additional detections are located around existing aggregations. The neural network approach indicates that 2.20% of the pixels of the entire OLCI scene are contaminated by _Sargassum_ against 1.53% when considering the \(\delta\)MCI. The relative increase in the _Sargassum_ coverage is, thus, 43% when using the neural network in comparison to \(\delta\)MCI approach. Such an increase could be explained by the fact that the neural network is able to take into account the depths of the aggregations. The retrieval of FC using MCI is based on the red and NIR reflectances where pure seawater strongly absorbs the light. The value of the pure seawater diffuse attenuation coefficient (noted K\({}_{\rm dw}\) in m\({}^{-1}\)) at 681 nm is 0.45 m\({}^{-1}\)[37]. Since about 90% of the radiation exiting the water column typically comes from a depth layer of 1/K\({}_{\rm dw}\), the boundary maximum depth for which _Sargassum_ could be potentially detected from above water reflectance is, thus, not greater than 2 m. However, the physical approach proposed here relies on spectral reflectances ranging from the blue domain (400 nm) to the NIR. The fact that short wavelengths are taken into account in our approach enables increasing the depth layer for which _Sargassum_ might be detected. The value of K\({}_{\rm dw}\) at 400 nm is 0.0209 m\({}^{-1}\), thus theoretically allowing observing a depth layer up to 48 m.  
Figure 12: **(a)** Spatial distribution of FC (color bar in %) retrieved by the neural network approach for the OLCI scene acquired on 8 July 2017; **(b)** histogram of the _Sargassum_ occurrence (in %) and **(c)** cumulative histogram of the _Sargassum_ occurrence (in %) of depth retrieved by the neural network. The retrieval of FC and z values from the neural network and from \(\delta\)MCI (only FC for this latter index) has also been applied to other scenes of the dataset (Section 2.2). Table 3 summarizes the fractional coverage retrieved by both \(\delta\)MCI and neural network as well as the proportion of the coverage retrieved by the neural network between 2 m and 5 m depth. The coverage estimated using the neural network is higher by a factor of 2 than that derived using \(\delta\)MCI approach for all the scenes except for the scene acquired on 2 May 2021, for which the coverage is fairly similar for both methods. The relative increase in the coverage retrieval ranges from 0.7% to 68.6% for the scenes considered here when using the neural network in comparison to the current \(\delta\)MCI approach.