## 4 Neural Network Implementation to Speed-Up Satellite Data Processing
Since the MSE for both the test and validation phases starts to increase for the same epoch number, namely, 42 (Figure 10b), the neural network proposed here does not show any overfitting issues. The error on the FC component (Figure 10c) mostly ranges between \(-3\)% and \(+3\)%, which is satisfactory (i.e., lower than \(1\)%). The error on z (Figure 10d) is also satisfactory since the histogram, which is centered on the value 0, rapidly decreases beyond \(+0.5\) m, and below \(-1\) m. The performances of the neural network are also evaluated using a different and larger dataset than the one used during the learning process (the approach has been described in Section 3.1). The RMSE values are 0.3 mg m\({}^{-3}\) for Chl, 0.21 g m\({}^{-3}\) for NAP, 0.006 m\({}^{-1}\) for CDOM, 3.1% for FC, and 0.54 m for z. These RMSE values are close to those obtained previously when using the SRT inversion approach (Table 1). The neural network is then applied to OLCI derived surface reflectances using the study area (white square Figure 2) of the scene acquired on 8 July 2017. The spatial variation of the fractional coverage and the immersion depth of the _Sargassum_ aggregations are examined (Figure 11). The spatial distribution of FC is shown in Figure 11a for which FC values lower than 0.1%. Beyond 5 m depth, the _Sargassum_ occurrence does not sufficiently  
Figure 10: (**a**) Neural Network Architecture; (**b**) MSE per epoch for the training phase (blue), validation phase (green) and test phase (red); the histogram of the probability of the error (validation phase) (**c**) for FC component and (**d**) for z component. influence the sea surface total reflectance to properly enable their identification (Section 3.2). In addition, previous studies showed that _Sargassum_ are mostly found in the upper water column (from the surface to 4 m depth) [18; 30; 31; 32]. Then, FC is not determined in this study for depth values greater than 4.9 m. The same main aggregation patterns are detected when using the neural network (Figure 10(a)) relatively to the SRT inversion. The estimated z component appears lower when using the neural network approach (<2.5 m) (Figure 10(b)) relatively to the SRT inversion (Figure 7(b)), especially on the edges of the aggregations (about 3 m when using the SRT inversion). The variation of the depth retrieval within the _Sargassum_ aggregations is fairly smooth when using the neural network relatively to the SRT depth retrieval which exhibits a sharp increase on the edges of the aggregations. The neural network is then applied to the entire scene acquired on 8 July 2017. FC retrievals range from 0.1% to 34% over the entire area. The proportion of _Sargassum_ occurrences as a function of depth is shown Figure 11(b). The highest number of occurrences is observed between 0 and 0.5 m. More interestingly, about 80% of the _Sargassum_ are located in the depth range from 0.5 m to 4.9 m and 36% from 2 m to 4.9 m (Figure 11(c)), thus highlighting the great interest of considering immersed _Sargassum_ in the inversion process. Figure 11: Spatial distribution of (**a**) FC (%) and (**b**) z retrieved by the neural network approach applied to the OLCI data acquired on 8 July 2017, over the study area (white square in Figure 2). Note that the data for which FC is lower than 0.1% and/or the depth is higher than 4.9 m were filtered out. Figure 12: _Cont._