## 3 Assessment of the Developed Algorithm Efficiency in Some Case Studies
### North Sea  
For the LM-based algorithm validation, 20 HPLC _chl_ and Ferrybox turbidity (_tsm_) samples have been available. These parameters were measured using the NIVA Ferrybox system [30] onboard the passenger ferry MS Color Festival that was crossing the Skagerrak (eastern-most region of the North Sea) between Oslo (Norway) and Fredrikshavn (Denmark) twice per day. For HPLC _chl_ determination water samples were collected and analysed in the laboratory. In addition, the Ferrybox system measured _chl_ fluorescence and turbidity continuously along the entire ferry transect. The turbidity data were converted to _tsm_ and used in the validation as single point measurements coincident with the HPLC match-up locations. Data used for this study originated from 9 different dates through March to November, 2007. A comparison of concentrations of _chl_ and _tsm_ measured _in situ_ and retrieved by our LM-based algorithm from the MERIS data (Table 6) indicates that the LM-based algorithm provides a high level of correlation (0.89) between the retrieved and measured values for _chl_ and significantly less high, but still acceptable (0.46), for _tsm_ with the RMSE of about 1.0 and 0.47 respectively. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|} \hline \multirow{2}{*}{**Time gap**} & \multicolumn{4}{c|}{_In situ_ measurements} & \multicolumn{4}{c|}{**Remote sensing data**} \\ \cline{2-7}  & _chl_, **ug/L** & _doc_, **mgC/L** & _sm_, **mg/L** & _chl_, **ug/L** & _doc_, **mgC/L** & _sm_, **mg/L** \\ \hline \multirow{6}{*}{**SaaWiFS**} & 4.23 & 10.5 & 0.8 & 5.2 & 14.2 & 0.5 \\  & 9.00 & 12.0 & 1.1 & 12.0 & 14.8 & 2.7 \\  & 17.89 & 13.5 & 4.5 & 12.1 & 14.7 & 3.2 \\ \cline{2-7}  & \multicolumn{4}{c|}{**r**} & **0.77** & **0.77** & **0.69** \\ \cline{2-7}  & \multicolumn{4}{c|}{**RMSE**} & **3.8** & **2.7** & **1.2** \\ \hline \multirow{6}{*}{**SaaWiFS**} & 15.18 & 14.3 & 3.1 & 13.5 & 14.9 & 1.1 \\  & 7.32 & 9.0 & 0.9 & 18.6 & 14.7 & 1.9 \\  & 13.12 & 8.6 & 0.9 & 18.0 & 14.8 & 2.3 \\  & 9.97 & 8.3 & 1.0 & 18.0 & 7.3 & 1.1 \\  & 7.31 & 8.3 & 1.2 & 8.1 & 9.0 & 1.5 \\  & 7.58 & 7.9 & 0.8 & 10.0 & 8.1 & 1.3 \\ \cline{2-7}  & \multicolumn{4}{c|}{**r**} & **0.29** & **0.56** & **-0.46** \\ \cline{2-7}  & \multicolumn{4}{c|}{**RMSE**} & **6.1** & **3.4** & **1.1** \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of WQP concentrations measured _in situ_ and retrieved from SeaWiFS data for the Baltic Sea, eastern Gulf of Finland, August 2002. It is worthwhile to compare the efficiency of the developed LM-based algorithm with the efficiency of other algorithms suggested for MERIS data processing (Table 6). Algal_1 and algal_2 are the products of standard ESA algorithms for estimation of the _chl_ concentration in, respectively, oceanic (case 1) and coastal (case 2) waters. As Table 6 indicates, the LM-based algorithm with the developed dedicated hydro-optical model performs appreciably better, both in terms of the explained variance and RMSE as compared not only with the oceanic algal-1 but also the coastal algal-2 algorithm. A validation of the MERIS standard products and some other alternative algorithms has been conducted recently by [31]. As an alternative approach for evaluating the performance of the LM-based algorithm, the values of phytoplankton chlorophyll fluorescence measured by the Ferrybox system were used to estimate a proxy for the chlorophyll concentration (\(chl_{F}\)) along the whole transect using a linear regression between concurrent measurements of HPLC _chl_ and sensor _chl_ fluorescence. It is important to note that the use of \(chl_{F}\) do not qualify for a proper validation of the satellite _chl_ products, as the conversion