## 4 Discussion  
Monitoring and managing aquatic resources is necessary to ensure the safety of marine environments. The MSFD sets out rules for all European member states to organize efforts to monitor and improve their coastal areas. Under the MSFD, Estonian coastal waters need to be monitored and GES must be reached and maintained [24]. The formation of HABs by cyanobacteria is a parameter for estimating the ES of an area and is one part of the larger monitoring scheme for the MSFD and the national environmental monitoring scheme in Estonian waters [62; 63]. The purpose of the assessment is to obtain the current status of the Estonian coastal areas as input for improving the conditions of the areas--where to focus adaptation and remediation efforts. The use of satellite data in the assessment could be of benefit to monitoring programs as a quicker and more extensive tool for the short- to long-term monitoring of coastal waters. An additional benefit of satellite data to the public is the quicker availability and spatial coverage of information about HABs in contrast to in situ sampling. The first goal of this study was to assess the applicability of the C2 RCC and POLYMER processors, which have previously shown good results in regions influenced by high phytoplankton biomass (high chl-a), CDOM, and TSM [56; 64; 65; 66]. C2 RCC has been shown to be useful in many different water bodies, for example, in the Baltic Sea [47] or in Estonian and Latvian lakes [67]. However, the Baltic Sea is an optically complex water body, where atmospheric correction and bio-optical modeling are difficult as evidenced by previous results by Toming et al. [57] and Kratzer and Plowey [56] in coastal areas in the Baltic Sea, where chl-a is typically lower and where influence by CDOM is high [47; 68]. Nonetheless, C2 RCC and POLYMER have previously shown the most promise for chl-a detection which was the reason for their selection here. Our validation of the two satellite data processors provided rather poor match-ups with the in situ chl-a (Figure 6). Our data also showed that very high chl-a (>20 mg m\({}^{-3}\)) estimated by C2 RCC was often highly turbid (>15 FNU) with low corresponding in situ chl-a (<10 mg m\({}^{-3}\)) (Figure 6). This makes it difficult to accurately estimate chl-a in highly turbid areas, like the Parnu, Matsalu, or Haapsalu bays. Similar to Kyryliuk et al. [47], C2 RCC tended to underestimate high chl-a at points where the turbidity was low (<5 FNU) (Figure 6). Although the POLYMER validation provided a higher coefficient of determination (R\({}^{2}\) = 0.39), the processor flags significantly more pixels. This is an issue when the goal of using satellite data for an environmental status assessment is to cover an area of interest as frequently as possible. One reason for the poor results received from our validation analysis is the difference in the collection methodology of chl-a. The satellite data consist of pixels covering an area of 300 m \(\times\) 300 m, whereas the in situ data consist of depth-integrated point measurements. The current way of gathering in situ samples is via ships which has previously been criticized as mixing the water column and disrupting the surface bloom, providing results that are not in accordance with the actual conditions [20]. Additionally, the current methodology aggregates chl-a from depths of 0-10 m below sea level which is an issue in cases where prevalent surface and subsurface blooms or turbidity of the water hinder light penetration to those depths. _Nodularia spunigena_, for example, forms dense surface blooms [31]. In order to improve the accuracy of chl-a detection, advances in bio-optical modeling and the use of hyperspectral sensors could be future research areas. Comparing N\({}_{2}\)-fixing cyanobacteria biomass gathered in situ, it was observed that biomass from satellites tended to overestimate the actual biomass measured at sampling stations.