## 4 Discussion  
Merging the databases of two recent projects has provided us with a large amount of data to carry out the validation of Rrs and water quality algorithms. The database covers a wide gradient of immunological variables but is always within the wide range used to develop the algorithms. It is a database representative of the variability of the climatic and immunological conditions of the Mediterranean Basin, with a greater representation of semi-arid environments. Regarding cross-correlations between the variables, similar to the data used to develop the water quality algorithms by in situ spectrometry, the OACs mainly affecting water transparency are TSS and phytoplankton pigments and as a consequence, have greater influence on the spectra of the studied water bodies, while CDOM has less influence on water transparency and the spectral features of the dataset. This higher or lower influence on the water spectrum facilitates or hinders its determination through remote sensing. Rrs validation has been performed for four types of ACs, three of them, the C2-Nets based on NN, and Polymer, a physical model based on a spectral optimization method. The C2-Nets use the top-of-atmosphere full spectrum as the input, while Polymer recovers the radiation scattered and absorbed by water from the measured signal by satellite sensors from the blue to the near-infrared spectral range. Considering the ACs models characteristics tested with our data and the validation results, with the Polymer program, we obtain the best validation results for the visible region, precisely the spectrum part used to apply the spectral matching method, and the worst in the NIR according to [PERSON] et al. [12], [PERSON] et al. [20], and [PERSON] et al. [33]. Regarding the C2-Nets, using the top-of-atmosphere full spectrum as the input, except for C2 RCC, both C2X and C2 XC obtain better validation results than Polymer in the Red-edge and NIR bands. C2-Nets differences are consistent in the used trained ranges of IOPs. With our data, the C2-Net, C2 XC-using intermediate IOPs ranges achieve the best validation results for all bands, except for R740 and R865, whose best results are obtainedwith C2X. C2X improves its results for longer wavelength bands because its trained ranges of scattering coefficients of typical sediments and white particles (calcaceous sediments) include extreme cases, better reproducing the increased scattering in the NIR and demonstrating their best results for turbid waters, agreeing with [PERSON] et al. [12], [PERSON] et al. [34], and [PERSON] et al. [35]. Another important aspect to take into account is the restrictiveness of the respective quality flags. According to this criterion, the Polymer program has obtained the highest number of match-ups with respect to the other ACs, according to [PERSON] et al. [17], [PERSON] et al. [33], [PERSON] et al. [20], [PERSON] et al. [12], and [PERSON] et al. [21]. Regarding C2-Nets, applying quality flags and match-up exercises, fewer data were removed with C2 RCC because it is trained for a lower IOPs range and coastal waters, and as can be seen in Figure 2, most of the samples have low concentrations of IOPs. However, its validation results were the worst for all bands except for \(R560\) and \(R665\), for which C2X had the worst results. Meanwhile, the most restrictive AC, and therefore the one with fewer data for the validation process, was C2X because it is trained for a high IOPs range, and our dataset has few samples with high IOPs concentrations. Therefore, for our dataset, the reduced number of valid match-ups and lower consistency in the accuracy along the spectrum observed with this processor made it the most uncertain C2-Net, agreeing with [PERSON] et al. [32]. These results indicate that the C2-Nets development is improving and adapting their training range for inland waters, although the sun glint handicap has not yet been solved. The results illustrate well that the ACs developed from NN are more limited to the water conditions of their training database.