## 4 Discussion  
In general, the spectral coefficient Rrs(\(\lambda\)) does not have anomalous extremes. It should be noted that according to the results of the in situ AERONET and satellite data validation, a high correlation proficiency for the Black Sea stations (Galata_Platform and Gloria (from 2019 Section_7)), varying from 95 to 98% depending on the spectral channel, was found. However, none of the considered dates in [33] corresponded to the days on which dust transport occurred, which does not fully reflect the statistics. This study reveals the need for validation, taking into account dust aerosol, to identify trends in the distortion of Rrs(\(\lambda\)) spectra from satellite data. From Tables 1-5, it can be seen that AOT and the Angstrom parameter coincided on days on which the atmosphere was clean and days on which dust was present, but in the presence of Sun glint, the satellite data overestimated AOT, decreasing the Angstrom parameter by more than 3 and 2.5-fold. Similar tendencies in the variability of the spectral brightness coefficient values of the Black Sea during dust transfer were obtained by [PERSON] and described in 2008 [17]. Then, a study was also carried out on two dates (clean atmosphere and dust) for the SeaWIFS and MODIS satellites with a comparative analysis of AERONET data. Negative values for the Black Sea brightness coefficients in the shortwave region (412 nm) were obtained in a similar way. Thus, it follows that since 2008, the problem has not been resolved. Similar results regarding an underestimation of the brightness coefficient of the Black Sea, depending on the presence of Sun glint, are described in [18], where the authors note that this problem occurs for both MODIS and VIIRS.  
Moreover, the introduction of error flags in dusty pixels remains particularly relevant, since they are capable of fully distorting the spectra. In [22], it was demonstrated that at the upper boundary values of AOT (>0.3), with dust transport, the values of the spectral brightness coefficient of the sea are greatly overestimated while for the Mediterranean Sea, they remain unchanged. Unfortunately, the ABSAER flag, indicating the presence of an absorbing aerosol, is generally not taken into consideration because the detection  
\begin{table}
\begin{tabular}{c c c} \hline  & **10.02.2021 (Section_7)** & **27.02.2021 (Section_7)** \\ \hline Lwn (412 nm) & 0.42815 & 0.241267 \\ \hline Lwn (443 nm) & 0.559174 & 0.464439 \\ \hline Lwn (490 nm) & 0.814275 & 0.675236 \\ \hline Lwn (560 nm) & 1.218337 & 0.988313 \\ \hline Lwn (667 nm) & 0.285271 & 0.282637 \\ \hline \end{tabular}
\end{table}
Table 5: The nearest time for satellite images’ Lwn(\(\lambda\)) values of in situ measurements at the Section_7_AERONET station on 10.02.2021 (no dust) and 27.02.2021 (dust).  
algorithm is still unreliable, and from 2012, it has been completely removed. Perhaps, based on the combinations of other error flags, it will be possible to detect dusty pixels in satellite images.  
Moreover, this study shows the need not only for analysis but also the discovery of tools to predict dust transport events over the Black Sea region. The authors of [23] describe one such forecasting method.