## 4 Discussion
It has been previously documented that the MCI works better with higher chl-a [51], whereas chl-a in Estonian coastal areas is relatively low with seasonal dynamics responsible for high peaks (IQR = 2.8-6.1 mg m\({}^{-3}\), median = 4.1 mg m\({}^{-3}\)). However, the use of the MCI for determining cyanobacteria biomass from satellite data is likely to be the reason for high satellite-derived biomass values in the Parnu, Matsalu, and Haapsalu bays, as high chl-a was present in these areas. Because the MCI is high when there are surface planktonic blooms or aquatic vegetation, it does not discriminate between groups of phytoplankton or other vegetation and has been used to monitor, e.g., _Sargassum_ and algal blooms in the Baltic Sea and the Arctic [69; 70]. Nonetheless, the MCI-derived values are best suited to compare different areas and to map potential bloom areas. Ourresults showed that the dynamics of the blooms when the biomass is above 300 mg m\({}^{-3}\) can be monitored. The differences between the in situ and EO data are a result of the differences in both methodologies. In situ sampling is more accurate at a singular point but does not give an overview of the entire area, whereas satellite-derived biomass covers a larger area but suffers from the inability to detect low biomass values in the presence of high CDOM (e.g., absorption at Parnu Bay 1.38-14.08 m\({}^{-1}\)) [40] and TSM (e.g., Parnu Bay 5-24.3 g m\({}^{-3}\)) [71]. In situ biomass sampling also suffers from the same drawbacks as the sampling of chl-a discussed above--besides more frequently monitored areas, there is a lack of consistent data for others. Even then, our cluster analysis has shown seasonal variability in the chl-a and turbidity which would have been missed without the satellites, adding to the usefulness of using EO data for monitoring. Regarding the difficulty of deriving biomass from satellite data, cyanobacteria are typically estimated in the form of an index [72]. Developing more accurate algorithms for the detection of cyanobacteria from satellites is necessary. Difficulties in assessing cyanobacteria from satellite data, especially in areas with low biomass, are not unique to the method used here but are known issues with current remote monitoring techniques as evidenced in [51; 72; 73; 74]. The indices are largely based on satellite-derived chl-a with additional inputs from turbidity and cyanobacteria biomass. The CSA, CyaBI (in situ), and CyaBI (satellite) showed comparable results for the coastal areas (Figure 9). Chl-a is used as a proxy for all types of phytoplankton and aquatic vegetation biomass [28]. Because the PII is solely based on chl-a, it gives the least additional information about the presence of cyanobacteria biomass. The three species of cyanobacteria included in our analysis, _Aphanizomenon_, _Nodularia_, and _Dolichosperum_, did not correlate well with the in situ chl-a (the best result being _Aphanizomenon_, R\({}^{2}=0.15,p>0.05\)). As such, on its own, using the PII is the least useful for coastal areas, where other vegetation or a bloom event consisting of other species of phytoplankton are present. Other indices do not show significant differences in the results of the Haapsalu, Matsalu, and Parnu bays and the rest of the coastal areas, whereas the PII does. Originally, chl-a was intended to be used as a proxy for cyanobacterial biomass and turbidity was intended to indicate the presence of dead cyanobacteria cells in open sea areas [17]. In calculating the CSA Index, turbidity is used as an input to classify cyanobacteria blooms in pixels. In the case of Estonian coastal areas, not only is turbidity caused by organic material but is most likely dominated by mineral particles, especially in the case of shallow areas and strong wind events [75]. For this reason, in Estonian coastal areas, the CSA Index, like other indices, is limited in differentiating between cyanobacteria biomass and other phytoplankton.