## 4 Discussion  
### Error sources analysis  
As seen in Section 2.5, the pivotal element of the developed algorithm is the employed hydro-optical model, so that the accuracy of the concentration vector retrieval is controlled (apart from the atmospheric correction) by the adequacy of that model. Bearing in mind that the employed hydro-optical model has been specifically developed only for one water body, and further tentatively used for another one (as in the case of the White Sea, Gulf of Finnland, Lake Michigan or Lake Huron), it would hardly be reasonable to expect a closer correspondence between the _in situ_ and retrieved data.  
Despite a frequent lack of synchronality between shipborne measurements and satellite overflights (At sometimes surpassed 10 hours), the retrieved and measured data compare pretty well as is illustrated in Tables 4-8. The closest conformity between these sets of data is observed for the coinciding dates when there is a chance that the field of _chl_ remains more or less unchanged. Conversely, this agreement deteriorates as the time difference grows.  
When analysing Tables 4-8, it should be kept in mind that each _in situ_ measurement relates to a single point (measuring less than 1 m by 1 m) on the water surface, whereas the satellite signal, from which the CPA concentration vector is retrieved, refers to a pixel measuring about 1 km by 1 km. Although the pixel area encompasses the point at which the _in situ_ measurement has been performed, this fact, nevertheless, implies that the retrieved CPA concentration vector is a surface-averaged value for the processed pixel. Understandably, in the case of pronounced horizontal and vertical heterogeneity of CPA distributions (which is a very common case), the departures between the _in situ_ measurements and retrieved CPAs are inevitable. Naturally, this applies to all intercomparison/validation case studies discussed throughout the text.  
Figure 7: Cont.  
As seen, in the case of the North Sea the correlation between the retrieved _chl_ and _chl\({}_{F}\)_ are significantly higher for the night-time cruises than for the day-time ones. Given that the satellite overpasses the study area at about 11 am local time, the explanation is thought to be residing in the sinusoidal diurnal cycle of the phytoplankton fluorescence (Figure 8, [41]): the fluorescence signal is minimum at the local noon, and its maximum falls on the local midnight. The ratio between _chl_ and fluorescence varies on the same diurnal scale, due to the varying photosynthetic state of the phytoplankton cells. Thus the correlation between _chl_ concentration measured by the satellite and fluorescence measured by the Ferrybox system varies accordingly.  
In the Lake Michigan case study it is evident that a poorer correspondence for _sm_ and _doc_ is due to the fact that we had to compare them with total suspended substance (TSS), and total organic carbon (TOC) that necessarily differ from _sm_ and _doc_ envisaged by the Lake Ontario hydro-optical model. Obviously, correspondence between _sm_ and _tsm_ can be poor, for instance, under conditions of an enhanced growth of phytoplankton. By definition, _toc_ encompasses both particulate and dissolved fraction of organic carbon [32], therefore correspondence between _doc_ and _toc_ can also be poor. However, even under such conditions the algorithm explains 0.83 of _chl_ variance.  
It should also be mentioned that a poorer correspondence for _sm_ concentrations measured in the White Sea is most likely due to a filter with coarse pores used for water sample processing and _sm_ extraction.