## 6 Conclusions  
The current methods used for the detection of _Sargassum_ aggregations from satellite data over open ocean waters rely on spectral reflectance indexes (e.g., MCI index) that emphasize the _Sargassum_ optical signature in the red-edge and the near infrared bands. However, since the water strongly absorbs in the NIR domain, those indexes are ineffective when the _Sargassum_ are immersed in the water column. The semi-analytical radiative transfer model developed by [PERSON] et al. [20] has been recently adjusted to take into account _Sargassum_ contaminated pixels for which the aggregations could be located at a given immersion depth z [16]. Such an adapted model was referred here as SRT model. The originality of this study is to invert the surface reflectance as derived from satellite data using the SRT model to retrieve the bio-optical parameters such as the _Sargassum_ aggregation features (FC and z). The inversion is carried out using an optimization process between the OLCI derived surface reflectance and the simulated surface reflectance from the forward version of the SRT model. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Biomass (Million Tons)**} \\
**Date** & \
\begin{tabular}{c} **\(\delta\)MCI Index** \\ **(Surface Waters Only)** \\ \end{tabular} &
\begin{tabular}{c} **Neural Network** \\ **(Surface + Water Column)** \\ \end{tabular} \\ \hline
8 July 2017 & 3.1 & 5.6 \\
27 May 2018 & 4.0 & 13.0 \\
14 June 2020 & 1.9 & 3.5 \\
14 September 2020 & 4.5 & 12.3 \\
28 December 2020 & 1.6 & 3.1 \\
2 May 2021 & 6.3 & 6.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the _Sargassum_ biomass (in million tons) derived from both the \(\delta\)MCI and the neural network approaches for the OLCI image dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Coverage (km\({}^{2}\))** \\ **\(\delta\)MCI Index (Surface** \\ **Waters Only)** \\ \end{tabular} & \begin{tabular}{c} **Coverage (km\({}^{2}\))** \\ **Neural Network (Surface \\ **+ Water Column)** \\ \end{tabular} & \
\begin{tabular}{c} **Relative Coverage** \\ **Difference (in \%)** \\ **between the Neural** \\ **Network and \(\delta\)MCI** \\ \end{tabular} &
\begin{tabular}{c} **Proportion (in \%) of Coverage** \\ **between 2-5 m Depth** \\ **(Neural Network Approach)** \\ \end{tabular} \\ \hline
8 July 2017 & 933.6 & 1666.0 & 43.9\% & 51\% \\
27 May 2018 & 1207.3 & 3880.1 & 68.8\% & 39\% \\
14 June 2020 & 558.6 & 1055.6 & 47.1\% & 44\% \\
14 September 2020 & 1340.2 & 3674.1 & 63.5\% & 31\% \\
28 December 2020 & 488.0 & 932.3 & 47.6\% & 30\% \\
2 May 2021 & 1884.3 & 1871.5 & 0.6\% & 42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the _Sargassum_ coverage (in km\({}^{2}\)) estimated from the neural network and the \(\delta\)MCI approaches for the OLCI image dataset. The performances of the SRT inversion retrieval were first estimated using a simulated dataset since in situ data were not available. Typically, the RMSE values were lower than 1% for FC and about 0.54 m for z, which is satisfactory. The proposed approach was then applied to Sentinel-3/OLCI sensor data. A relative increase of 31% in _Sargassum_ coverage was found when comparing to the coverage that is currently estimated using the MCI algal index. It was shown that the aggregations are located deeper in the water column at the edge of their spatial area while their fractional coverage (or abundance) is weaker. The inversion process that relies on the SRT physical model is highly time consuming, typically about 10 days for a typical OLCI image (4090 \(\times\) 4865 pixels); therefore, a neural network approach was used to make more operational the application of the proposed method to a large dataset. The neural network was trained using a synthetic dataset. Seven Sentinel-3/OLCI scenes that were acquired at various years and seasons were examined.