## 2 Methods
### LM-Based Hydro-Optical Retrieval Algorithm  
The principal flow-diagram of our LM-based algorithm is given in Figure 1. An atmospherically corrected satellite image from SeaWiFS or MODIS is used to calculate the corresponding subsurface remote sensing reflectance spectrum \(S(\lambda)\)for each pixel [10] making use of the normalized upwelling radiance \([L_{w}(\lambda)]_{N}\). Images from these sensors are downloaded from the NASA OCEANCOLOR web site [18] in Level-2 format. MERIS image processing includes calculation of \(S(\lambda)\) from the provided values of remote sensing reflectance above water \(R_{rs}(+0,\,\lambda)\). These images are downloaded either from the ESA rolling archive [19] or from the MERCI catalogue [20] in Level-2 format. \begin{table}
\begin{tabular}{|c|l|c|c|c|c|c|} \hline \multirow{2}{*}{\begin{tabular}{c} **Error** \\ **type** \\ \end{tabular} } & \multirow{2}{*}{\(C_{dec}\)**, mgC/L**} & \multicolumn{5}{c|}{\(C_{sm}\)**, mg/L**} \\ \cline{3-8}  & & **0-0.5** & **0-5-1** & **1-5** & **5-10** & **10-20** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} **Error** \\ **type** \\ \end{tabular} } & 0-2 & 0-30* & 0-30 & 0-30 & 0-30 & 0-30 \\ \cline{2-8}  & 2-5 & 0-30 & 5-30 & 0-30 & 0-30 & 0-30 \\ \cline{2-8}  & 5-10 & 0-30 & 0-30 & 0-30 & 0-30 & 0-30 \\ \cline{2-8}  & 10-20 & 0-30 & 5-30 & 5-30 & 0-30 & 5-30 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Error** \\ **type** \\ \end{tabular} } & 0-2 & 0-5 & 0-5 & 0-5 & 0-5 & 0-5 \\ \cline{2-8}  & 2-5 & - & - & 0-5 & 0-5 & 0-5 \\ \cline{1-1} \cline{2-8}  & 5-10 & - & - & - & 0-5** & 0-5 \\ \cline{1-1} \cline{2-8}  & 10-20 & - & - & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 2: Ranges of \(C_{chl}\), within which the _chl_ retrieval accuracy specified in Table 1 is attainable given various options of _doc_ and _sm_ abundance in water and the spectral behavior of noise. Single- and double-asterisked entries exemplify the water composition situations that are, respectively, favourable and unfavourable for remote sensing of _chl_. In the next step, the spectral curvature of \(S(\lambda)\) is automatically analysed to eliminate pixels with _a_) negative values of \(S\) at short wavelengths (400-450 nm) due to overestimation of the path radiance, and _b_) enhanced values of \(S\) in one or two first sensor channels followed by a dip in either the second or third channel due to underestimation of the path radiance. Further, the algorithm calculates the gradient of the processed \(S\) spectrum starting from the first channel (i.e. the shortest wavelength) and moving to the red portion of the spectrum (towards the longest wavelength in the visible). The pixel is preserved only if the corresponding spectrum exhibits a stable positive gradient at \(\lambda<\sim 560\) nm (it is allowed to be just non-negative at \(\lambda\leq 450\)nm), followed by a negative gradient for \(\lambda>\sim 560\) nm. The pixel is flagged, if it does not satisfy this requirement. However, this mask does not discard pixels with the \(S\) spectra characteristic of clear waters, should they be present in the image. Identification of clear and turbid waters is performed through jointly analyzing both the envelope of \(S\) spectra and their mean spectral value. The latter is known [21] to be distinctly different for clear and turbid waters, and a corresponding threshold is applied to this end. For the remaining pixels the derived \(S\) is sent to the LM unit. The algorithm code is designed in such a way that there is a possibility of choosing an appropriate hydro-optical model from the  
Figure 1: A flow-diagram of the developed algorithm performance. embedded collection of hydro-optical models. The LM unit yields simultaneously the concentrations of _chl_, _sm_ and _doc_. The algorithm has a quality assessment/assurance unit whose function is to detect pixels corresponding to water areas whose hydro-optical properties significantly differ from those covered by the chosen hydro-optical model.