## 3 Results
### Input Parameters for Estimating Bloom Indicators  
#### 3.1.1 Chl-a and Turbidity  
Most of the in situ measurements showed chl-a in the range of 3.0-7.3 mg m\({}^{-3}\) (interquartile range IQR), whereas the satellite data showed a larger variation in the chl-a, 2.9-15.6 mg m\({}^{-3}\) (Figure 3). The satellite-derived turbidity had a range of values from 0.3 to 5.4 FNU (Figure 3). The Haapsalu, Matsalu, and Parnu bags showed much higher chl-a and turbidity than the other coastal areas. No apparent linear relationship between chl-a and turbidity was found in most of the coastal areas (Appendix B, Figure A2). This is even more evident when looking at the chl-a and turbidity throughout the phytoplankton spring and summer bloom period of April-September. Looking at the chl-a time series, differences between the three ways from each other and from the other areas can be observed (clusters 1, 2, and 3 in Figure 4). The peak in chl-a was observed in June in Matsalu Bay (cluster 3) but toward the end of August in Haapsalu Bay (cluster 1). Cluster 4, which included all the other coastal areas, did not exceed 10 mg m\({}^{-3}\) chl-a in 2022 (Figure 4). This suggests that a one-size-fits-all approach to estimating ES is not applicable to all coastal water bodies when basing it on chl-a and turbidity. The annual peak of chl-a in Parnu Bay likely occurs in the early spring as the chl-a is already highest in April and shows a downward trend from then on. According to the turbidity, the coastal areas were divided into six clusters (Figure 5). Clusters 3 and 5 showed low turbidity throughout the time series. In clusters 1 and 6, the areas were more turbid during the spring and early summer. Cluster 4, which includes the Parnu and Matsalu bays, showed a peak in turbidity before the start of our observation period which likely stems from higher river inputs during the early spring as a result of snowmelt. However, a second turbidity peak appeared at the end of the summer. Cluster 2, which exclusively included Haapsalu Bay, showed the highest turbidity values as well as the largest seasonal variability. There was a clear peak in May and another, even higher peak, at the end of August (Figure 4). The peak at the end of the summer is in accordance with a chl-a peak a few days before. This could be a possible indication of a significant bloom event in Haapsalu Bay in 2022 (Figure 4 cluster 1 and Figure 5 cluster 2). Figure 6 shows the comparison of the C2 RCC and POLYMER estimated chl-a and the measured in situ chl-a. The C2 RCC validation resulted in a total of 135 valid chl-a match-ups and only 46 for POLYMER. Both processors showed a weak relationship: C2 RCC R\({}^{2}\) = 0.2 (r = 0.45, df = 133, \(p\) \(<\) 0.05), and POLYMER R\({}^{2}\) = 0.39 (r(41) = 0.64, \(p\) \(<\) 0.05) (Figure 6). Similarly, previous research has also shown a poor correlation between the AC algorithms in the Baltic Sea [56; 57]. Although POLYMER showed a better relationship,  
there were fewer match-ups compared to C2 RCC. Previous work has also suggested that for the Baltic Sea region, C2 RCC is the best available processor for chl-a estimates [58]. Because of these reasons, it was decided to proceed with the C2 RCC data products. Figure 4: K-means clustering of coastal areas based on daily chl-a in 2022 from April to September. Centroids are given in yellow. Cluster 1: Haapsalu Bay; Cluster 2: Párnu Bay; Cluster 3: Matsalu Bay; Cluster 4: all coastal areas except for the three mentioned in other clusters. Figure 3: Satellite-derived chl-a (mg m\({}^{-3}\)) and turbidity (FNU) in coastal areas in 2016–2022. Boxes represent interquartile range (IQR), middle line represents median, whiskers show the maximum and minimum values, and black dots represent outliers. Figure 5: K-means clustering of coastal areas based on daily turbidity in 2022 from April to September. Centroids are given in yellow.