## 4 Discussion
Additionally, the relationship between chl-a and turbidity is not linear in most coastal areas (Appendix B, Figure A2), and according to our cluster analysis, a peak in chl-a is not followed by peaks in turbidity as would be expected if dead cyanobacteria cells were present in the area after a chl-a peak. One exception is Haapsalu Bay, where a peak in chl-a is followed by higher turbidity (Figure 4 cluster 1, and Figure 5 cluster 2). However, the shallow parts of Haapsalu Bay are dominated by bottom vegetation covering parts of the sea which could also explain the high chl-a picked up by the satellite. Although supplementing turbidity and chl-a with cyanobacteria biomass would lead to more accurate estimates, in the absence of such data, the CSA Index could be used for Haapsalu Bay with consideration for possible bottom vegetation influence, especially in the late summer. Estimating chl-a and turbidity from satellite data allows for spatial and temporal coverage not possible with in situ measurements. However, these parameters have deficiencies regarding the accurate estimation of cyanobacteria biomass [17; 28]. Our results indicate a similar deficiency as areas with high chl-a and turbidity showed values indicative of cyanobacteria blooms under CSA and the PII but did not show high N\({}_{2}\)-fixing cyanobacteria biomass. This resulted in more favorable CyaBI (both in situ and satellite) values in 2022 for the Parnu, Muuga-Tallinna-Kakumae, and Narva-Kunda bays and the NE Gulf of Riga compared to the CSA and PII values in those areas (Table 3). Because of this, the most applicable index is the CyaBI in coastal areas which are naturally turbid and have a lot of chl-a(Parnu, Matsalu, and Haapsalu bays). Both the satellite-derived and in situ CyaBI have their merits over the other. The use of the CyaBI (in situ) is most applicable in coastal areas where satellite-derived N\({}_{2}\)-fixing biomass is the most difficult to assess, such as the Parnu, Matsalu, and Haapsalu bays. These areas are dominated by CDOM and TSM. This index benefits from the use of both in situ cyanobacterial biomass and satellite-derived turbidity and chl-a. In situ measurements, however, are sparse, as evidenced in the frequency of the measurements in Table 1. It would be advisable to supplement the satellite-derived chl-a and turbidity estimates with in situ cyanobacteria biomass, but in areas where turbidity is less of a factor (<1 FNU), the use of satellite-derived biomass is possible e.g., the Hara and Kolga, Eru-Kasmu, or Muuga-Tallinna-Kakumae bays, and would provide better temporal coverage. In such areas, the CyaBI (satellite) could be used. An additional goal of this study was to assess the current ES in the Baltic Sea on the Estonian coast during the summer cyanobacteria bloom events from 2016 to 2022 using four indices that have previously been applied in the region. As mentioned in Section 3.3, there were five coastal regions that did not achieve GES in 2022 under any of the four methodologies used in this study. It is unlikely to be an accurate estimate of ES in terms of describing cyanobacteria blooms, but rather a possible reason for these results could be the spatial and hydrological effects dominating these coastal areas. The Haapsalu, Matsalu, and Parnu bays are heavily influenced by river and wetland inputs and wave dynamics, which increase the turbidity of the bays [40; 76; 77; 78]. Particularly during windy days, the shallowness of these areas leads to high concentrations of resuspended bottom sediment in the water column [40]. High turbidity from sediments rather than dead cyanobacteria cells is one possible explanation for not achieving GES in these areas. From our analysis, higher satellite-derived chl-a was more evident when the turbidity was also higher even though the corresponding in situ-sampled chl-a was not (Figure 6A, where turbidity > 20 FNU). It also highlights why using the PII in turbid areas is disadvantageous as turbidity can inhibit accurate chl-a estimation.