## 4 Discussion
Polymer may also have limitations due to its internal marine model, depending on chlorophyll and sediments, although its present high performance shows suitability for our water types. The inconvenience with Polymer is the difficulty to process images for a non-professional user since it is not a program with a user-friendly interface. Regarding the water quality algorithms validation, the best results were obtained with the ACs with the best Rrs validation results, C2 XC and Polymer. The best-validated algorithm for SDD estimation uses the Blue/Green ratio (R492/R560) applied to Polymer Rrs. It is the algorithm with the shortest wavelength bands of all tested algorithms, and therefore the bands with the fewest errors in the validation of the Rrs. The Blue/Green ratio was the second-best result in the previous study, with results very similar to ours, with an R\({}^{2}\) of 0.65, an RMSE of 1.21 m, an RRMSE of 51%, and a bias of 0.47 m, which were obtained for a 0.14-9.55 m range. In our validation results, the RMSE is slightly higher (1.59 m), although the RRMSE is lower (47%) for the same range of data. The results also agree with the RMSE of 1.4 m obtained with 82 samples in the study carried out by [PERSON] et al. [36], using the same AC and relation bands. The good results also obtained with C2 XC (Table 10) indicate that the algorithm obtained from 266 in situ reflectances is applicable and reliable for ACs that achieve an accurate estimate of water-leaving radiance. The best-validated algorithm for CDOM estimation, based on the Red-edge1/Blue ratio (\(R704/R492\)) and applied to C2 XC Rrs, was the algorithm with the biggest errors in the previous study. However, the validation results were very similar among the different algorithms on the previous study, obtaining for R704/R492 ratio an R\({}^{2}\) of 0.5, an RMSE of 1.03 \(\upmu\)g/L QSE, an RRMSE of 56% and a bias of 0.26 \(\upmu\)g/L QSE, for a data range between 0.3 and 5.3 \(\upmu\)g/L QSE. The values improved in our validation for R\({}^{2}\) (0.8) and RMSE (0.42 \(\upmu\)g/L QSE), while the bias (0.32 \(\upmu\)g/L QSE) is similar and the RRMSE (87%) is much higher because the range of data is much lower, 0.03-1.75 \(\upmu\)g/L QSE. These results are in agreeance with bands relations obtained in the study carried out by [PERSON] et al. [37], from simulated water-leaving radiance by Hydrolight and with the potential correlation between the Rrs and CDOM values obtained in [PERSON] et al. [38], [PERSON] [39], [PERSON] et al. [40], and [PERSON] et al. [41]. The bad results in contrast to the other variables could be due to the lesser influence of CDOM on the transparency for our database and consequently in the reflectance spectrum. Nevertheless, although the RMSE seems low, the algorithm should be tested with a larger CDOM range. For TSS, in situ reflectance algorithms for two data ranges were validated, below and above 20 mg/L, and in both groups, the best algorithm was obtained using the \(R704\) band and Polymer Rrs. Both algorithms are obtained with a lineal calibration correlation, but the slopes and offsets are very different. Of all the bands used in the tested algorithms, \(R704\) is not simply the band with lowest errors in the validation of the Rrs but it is the band with better results in the previous study. The best validation results for values below 20 mg/L in the Previous Study were also with \(R704\) band, with an R\({}^{2}\) of 0.85, an RMSE of 1.79 mg/L, an RRMSE of 43%, and a bias of 0.39 mg/L, very similar values to those obtained in this work, where all the validation results enhance except bias (0.74 mg/L). On the other hand, for values above 20 mg/L, with only five data points available for validation in the previous study, the \(R704\) band did not reach the best results. With these five data points, only an R\({}^{2}\) of 0.02, an RMSE of 85.97 mg/L, an RMSE of 190%, and a bias of 20.82 mg/L were obtained. The results greatly improved in this work, using 13 data points in the validation process.