The Baltic Sea (Figure 1) is a brackish shallow semi-enclosed basin characterized by large inputs of pollutants and nutrients from natural and anthropogenic sources combined with a limited water exchange with the open ocean through the Danish Straits in the southwest, causing large latitudinal gradients of salinity and dissolved organic matter (Omstedt et al., 2004; Leppäranta and Myrberg, 2009). As a consequence of the strong anthropogenic pressure, pollution (HELCOM, 2018), eutrophication episodes (Andersen et al., 2011; Fleming-Lehtinen et al., 2015; Heiskanen et al., 2019), and/or phytoplankton blooms (Wasmund et al., 2011; Kahru et al., 2018; Hjerne et al., 2019) threaten its fragile ecosystems. Growing concern about the basin’s health is raised by the Baltic Marine Environment Protection Commission (Helsinki Commission, HELCOM) (HELCOM, 2007). The Baltic Sea is characterized by high concentrations of colored dissolved organic matter (CDOM). Rivers are the main CDOM source, which follows a general dilution gradient from north to south with a large spatiotemporal variability driven by processes such as ice melting, rainfall, phytoplankton blooms, or photodegradation (Berthon and Zibordi, 2010; Ylöstalo et al., 2016; Simis et al., 2017; Kratzer and Moore, 2018).

Two seasonal phytoplankton blooms are usually observed in most areas of the Baltic Sea (Wasmund et al., 2011; Kahru et al., 2018; Brando et al., 2021). Firstly, a strong spring bloom dominated by diatoms and dinoflagellates is responsible for most of the annual primary production in the area (Simis et al., 2017; Zhang et al., 2018). This spring bloom progresses from south to north due to light and nitrogen limitation, and it can cause anoxia and hypoxia events in the bottom layer because of the fast diatom sedimentation (Hjerne et al., 2019). After a minimum production in early summer (May–June), phosphorus excess and increasing surface water temperature lead to the blooming of nitrogen-fixing cyanobacteria, causing extensive and prolongated surface and near-surface accumulations of filamentous species during calm weather periods in July and August (Kahru et al., 1994; Finni et al., 2001; Kahru et al., 2007; Kahru et al., 2018).

Chlorophyll-a (chl-a) concentration (measured in mg m⁻³) is one of the most relevant indicators for water quality monitoring within the Baltic Sea Action Plan implemented by HELCOM, as it is useful for assessing the eutrophication status and a good proxy for phytoplankton blooms (HELCOM, 2017; HELCOM, 2019; Ahlman et al., 2020). Compared with chl-a data from sampling stations, in-situ platforms, or automated ship measurements, chl-a maps derived from ocean color (OC) satellite images provide a synoptic view of the phytoplankton spatial distribution. Although data availability in terms of spatial coverage and temporal resolution is limited by the cloud cover, it can be significantly enhanced by merging data from different sensors (Groom et al., 2019; O’Reilly and Werdell, 2019; Sathyendranath et al., 2019).

Accuracy and reliability of chl-a retrievals from OC data depend on two related factors: 1) the optical characteristics of the water and 2) the performance of the atmospheric correction (AC) algorithms converting the spectral top-of-atmosphere (TOA) radiances measured by the satellite sensors to spectral remote-sensing reflectance (Rrs, defined as the ratio of the water-leaving radiance and the downwelling irradiance and measured in sr⁻¹) used as input in chl-a estimation algorithms (Brewin et al., 2015; Sathyendranath et al., 2019). Despite the good results in open-ocean and high-scattering coastal areas (Blondeau-Patissier et al., 2014; O’Reilly and Werdell, 2019), retrieval of reliable OC products from high-absorbing waters such as the Baltic Sea is a challenging task. High absorption coefficients related to the high CDOM concentrations (with aCDOM values exceeding 1.0 m⁻¹ at 440 nm, Ylöstalo et al., 2016) in combination with relative low sun elevation lead to low Rrs values, especially in the blue part of the spectrum. Therefore, AC algorithms show a limited performance producing inaccurate Rrs spectra with low and even negative values (Attilla et al., 2013; Beltrán-Abaunza et al., 2014; Alikas et al., 2020; Brando et al., 2021; Tilstone et al., 2022).

Regarding chl-a retrieval, standard blue-green band-ratio algorithms have been reported as not suitable for the Baltic Sea because they tend to a significant overestimation (Darecki and Stramski, 2004; Odermatt et al., 2012; D’Alimonte et al., 2016; Pitarch et al., 2016; Ligi et al., 2017; Kratzer and Moore, 2018). Better results have been achieved with regionalized blue-green ratios (Darecki and Stramski, 2004; Attilla et al., 2013; Ligi et al., 2017), red-edge bands (Ligi et al., 2017), or neural network (NN) algorithms based on different sets of Rrs values (Kratzer and Vinterhav, 2010; Hieronymi et al., 2017; Toming et al., 2017; Kyryliuk and Kratzer, 2019), although accuracy is still hampered by the optical complexity of the basin and the low performance of the AC processors.

Within the Copernicus Marine Service (CMEMS, Le Traon et al., 2019; von Schuckmann et al., 2022), two operational chl-a data streams are available for the Baltic Sea based on OLCI and on merged multisensor time series. These data streams are based on sensor merging to improve the daily spatial coverage at 300 m and 1 km resolutions to support the operational oceanography users and environmental reporting needs (Le Traon et al., 2019; Sathyendranath et al., 2019; von Schuckmann et al., 2022).

Brando et al. (2021) proposed a new ensemble approach based on multilayer perceptron neural network (MLP) bio-optical algorithms (ENS-MLP), with results outperforming those based on other methods reported in the literature. This new approach was implemented in a fully reprocessed multisensor time series of Rrs and chl-a data at ~1 km spatial resolution (OCEANCOLOUR_BAL_BGC_L3_MY_009_133, 2023).

This work documents the implementation of the new Rrs and chl-a level-3 datasets for the Baltic Sea based on the complete Sentinel-3 A and B OLCI time series (2016 to present) of OC images at full resolution (300 m), using the same ENS-MLP approach for chl-a retrievals. To this aim, the following steps were carried out: 1) the selection of the best AC processor to obtain OLCI level-2 Rrs from level-1 data, 2) the assessment of the new OLCI level-3 Rrs dataset and comparison with the CMEMS multisensor dataset at 1 km resolution, and 3) the comparative validation analysis of the multisensor and OLCI level-3 chl-a datasets based on the ENS-MLP approach. The validation results were based on several in-situ data sources: automated radiometry from the Aerosol Robotic Network-Ocean Color (AERONET-OC) sites in the Baltic Sea, shipborne hyperspectral radiometry collected by the Finnish Environment Institute (SYKE), and chl-a concentrations from Alg@line and COMBINE datasets.

The remainder of this document is structured as follows: Section 2 introduces the data and methods used in this work, describing the validation exercises; the results presented in Section 3 include a match-up summary, validation results for level-2 OLCI Rrs, level-3 Rrs and chl-a, and a comparison between CMEMS-OLCI level-3 and OC-CCI v.6 datasets. The discussion and concluding remarks are addressed in Sections 4 and 5, respectively.

This study presented the introduction within the Copernicus Marine Service of the operational Rrs and chl-a datasets for the Baltic Sea from OLCI full resolution (300 m). Poor performances have been reported in the assessment of OLCI Rrs for the Baltic CDOM-dominated waters using both the EUMETSAT Operational Baseline (Zibordi et al., 2018; Zibordi et al., 2022) and the alternative atmospheric correction processing chain based on CR2CC (Cazzaniga et al., 2022). Hence, the first step was to select the best AC method to retrieve OLCI Rrs by comparing the accuracy of four processors using in-situ radiometric data from AERONET-OC sites and Alg@line shipborne hyperspectral radiometry as reference.

Our validation results from both in-situ sources (fixed platform and shipborne observations) show that POLYMER v.4.14 was the best option for the implementation in the processing chain for the new level-3 OLCI Rrs and chl-a datasets (see Section 3.2). In fact, it performs better not only in the 443–665-nm spectral range which includes the relevant bands for chl-a retrieval (Table 1), but also at 400 nm, 412.5 nm, and 778.75 nm. A greater variability in the metrics in the 673.75–708.75-nm spectral range (validated only with shipborne radiometry) and at 865 nm hinders the identification of the best-performing AC in this spectral range. Overall, performance differences are more remarkable in the blue spectral region (400–490 nm), especially in terms of correlation (Figures 6, 9). The main drawback for chl-a retrieval is the positive bias observed from both sources across the 412.5–665-nm spectral range.

Although validation results from AERONET-OC sites are expected to be more robust and reliable as in-situ data come from a stable platform with fewer uncertainties, metrics based on shipborne radiometry collected in 2016 were consistent and show the potential of this method to increase the number of match-ups providing data at other sites with different atmospheric and/or water conditions. According to Tilstone et al. (2022), differences between both in-situ sources could be mainly explained by two factors: 1) site differences—Baltic Sea waters are mainly dominated by CDOM, but ship trajectories could be more influenced by increases in chl-a concentrations due to phytoplankton blooms, whereas CDOM concentrations are generally higher in AERONET-OC sites; and 2) instruments and data processing—the differences in instruments (TriOS-RAMSES in the case of the hyperspectral shipborne radiometry; CIMEL-SeaPRISM for AERONET-OC) with their specific uncertainties by wavelength may be augmented by the fact that the data are processed with distinct methodologies. Moreover, in our study, shipborne radiometry is only available for validating Sentinel-3A in 2016, as AERONET-OC in-situ data extend from 2016 to 2022 enabling the validation of both Sentinel-3A and Sentinel-3B.

In our study, POLYMER results from AERONET-OC as compared with Alg@line in the 442.5–665-nm spectral range were better in terms of correlation (0.41–0.90 vs. 0.19–0.61), but slightly worse considering RMSD (2.6–7.0 10⁻⁴ sr⁻¹ vs. 1.7–6.0 10⁻⁴ sr⁻¹) or bias (1.3–6.4 10⁻⁴ sr⁻¹ vs. 0.8–3.7 10⁻⁴ sr⁻¹). As RPD are similar from the two sources (between 10% and 28% except for 442.5 nm), higher RMSD or bias values using AERONET-OC could also be related to its larger in-situ Rrs range and maximum values (see in-situ distribution in Figures 4, 7). Moreover, overall better results according to all the metrics were derived from AERONET-OC at 400 nm, 412.5 nm, and 778.75 nm (except for RMSD at 412.5 nm). At 400 nm, a negative bias was obtained from Alg@line (−2.7 10⁻⁴ sr⁻¹) but a positive value (3.2 10⁻⁵ sr⁻¹) from AERONET-OC, which could be related to differences in optical water characteristics: higher CDOM concentrations leading to lower Rrs (and higher bias) in AERONET-OC sites. In fact, only 21% of match-ups from AERONET-OC show Rrs greater than 1 10⁻³ sr⁻¹ against 55% from the shipborne radiometry.

Another remarkable feature is the high RPD peak at 442 nm from shipborne radiometry, which is present in all the processors. This peak is explained by a small set of match-ups with very high RPD values (>200% with a maximum of approximately 23,000%) from 2 days (9 June 2016 and 28 July 2016). These high RPD values are caused by some outliers in the in-situ distribution at 442 nm characterized by very low Rrs values (0.06–0.86 10⁻³), as compared with the interquartile range between 1.25 10⁻³ and 1.75 10⁻³ sr⁻¹ (Figure 7). These results evidence that results from shipborne radiometry could be improved by a stricter in-situ quality control (e.g., removing spectra with outliers). However, this refinement is out of the scope of this work as we use the dataset as a method to confirm the conclusions based on AERONET-OC data.

POLYMER also shows advantages in terms of coverage in comparison with other AC methods as by design it is able to deal with residual sun glint (Steinmetz and Ramon, 2018). As seen in Table 3, the number of valid match-ups based only on POLYMER flag mask bitmask (846 valid match-ups from AERONET-OC) or combining bitmask and IdePix (773 valid match-ups from AERONET-OC) is 30% higher than those using other processors. However, yielding more match-ups does not imply a better or worse performance of the validation results. In fact, datasets showed similar distributions, with close ranges and median values across the spectra. Table 4, based on AERONET-OC in-situ data, presents comparable values for R², RMSD, or bias in the 412.5–665-nm spectral range, whereas expected higher uncertainties were observed at 400 nm or 778.75 nm. Note that bias was consistently lower across the whole spectra using only bitmask, especially in the blue, meaning that extra match-ups produced by POLYMER tend to show a lower bias.

Table 4 also shows the differences between the POLYMER validation metrics from Sentinel-3A and Sentinel-3B using in-situ data from AERONET-OC. Overall, results from Sentinel-3A are better considering all the metrics across the spectra, except for the bias between 620 nm and 778.75 nm. Differences are expected since match-up datasets do not show the same spatial–temporal coverage leading to different numbers of valid match-ups as Sentinel-3B is only available from 2018.

Our level-2 validation results agree with the findings in other works comparing AC algorithms over the Baltic Sea. Tilstone et al. (2022) assessed Sentinel-3A Rrs from WFR (pb 2.23–2.29 and OL_L2M.003), POLYMER v.4.14, and C2RCC using in-situ data from Alg@line shipborne radiometry (199 match-ups), Gustav Dalen Tower (5 match-ups), and Helsinki Lighthouse (4 match-ups), all the match-ups for only 2016. They found that POLYMER was the best-performing AC algorithm for six bands (412 nm, 443 nm, 490 nm, 560 nm, 665 nm, and 709 nm). Alikas et al. (2020) validated satellite OLCI Rrs from four AC processors (i.e., ALTNN, C2RCC, POLYMER, and WFR) against above-water field measurements collected from a research vessel over the coast of the Baltic Sea and Estonian Lakes in 2016. With a number of valid match-ups between 15 and 49 depending on the AC processor and filtering level, they reported POLYMER as the best suitable algorithm for all the OLCI bands except for 865 nm.

Since most of the valid match-ups (199 of 208) in Tilstone et al. (2022) are derived from the same dataset based on shipborne radiometry, metric values are expected to be similar to those reported in Section 3.2.2. In fact, RMSD values (2–6 10⁻⁴ sr⁻¹ in this work; 3–7 10⁻⁴ sr⁻¹ in Tilstone et al., 2022) or Pearson correlation coefficients (0.45–0.78 in this work; 0.38–0.6) follow a similar pattern. Differences are mainly observed in the bias, with negative values in Tilstone et al. (2022) (−4 10⁻⁴ to −1 10⁻⁴ sr⁻¹) instead of the positive bias found in our work (8 10⁻⁵ to 4 10⁻⁴). This disparity could be explained because we applied a stricter validation protocol with results based on a dataset of common match-ups, leading to a considerably lower number of match-ups (107 vs. 199 in Tilstone et al.). Relaxing our protocol using only bitmask as flag band, the number of valid match-ups increases until 251 and bias tends to be lower (likewise using the AERONET-OC dataset), so that comparable negative bias values (−8.9 10⁻⁵ to −1.4 10⁻⁴) were found in the 490–665-nm spectral range.

Regarding the level-3 datasets, the in-situ distribution for the complete OC-CCI v.6 time series (Figure 10A) is characterized by lower Rrs values at 490 and 560 nm in comparison with the OLCI period (Figures 10B, C). Differences are more remarkable at 560 nm, with an upper quartile value approximately 3.5 10⁻³ sr⁻¹ against a peak of almost 5 10⁻³ sr⁻¹. These discrepancies could be explained by two facts. Firstly, differences in the optical water types related to the data availability from the three AERONET-OC sites: the complete time series include more spectra from Helsinki Lighthouse, whereas in-situ data from Irbe Lighthouse (available since 2018) become more predominant during the OLCI period as measurements at the Helsinki Lighthouse ended in 2019. Secondly, the results at 560 nm for the complete time series use in-situ data from the AERONET-OC band at 555 nm available between 2005 and 2011, introducing uncertainties associated with the band-shifting process. However, the results for the OLCI period are only based on the AERONET-OC band at 560 nm introduced in 2018 (Table 1).

Using the common set of match-ups, CMEMS-OLCI and multisensor OC-CCI v.6 show equivalent distributions and metrics (with some differences indicated in Section 3.3) notwithstanding the different spatial resolution (300 m vs. 1 km). This similar behavior could be explained because both datasets are based on the same AC processor, i.e., POLYMER (with the exception of SeaWiFS), and that from 2020 onward, OC-CCI v.6 is based only on OLCI from Sentinel-3A and Sentinel-3B, as MODIS-AQUA and VIIRS are included only until the end of 2019 (OC-CCI, 2022). As expected, the metrics from CMEMS-OLCI L3 (Figure 6) are very close to those from POLYMER OLCI L2 (Figure 12).

Our results based on the complete OC-CCI v.6 time series differ from the metrics for OC-CCI v4.2 reported in Brando et al. (2021). OC-CCI v.6 performs better in terms of correlation for the 412–490-nm spectral range, whereas R² values are similar for the other bands, with more remarkable differences at 400 nm (0.51 vs. 0.05) and 442.5 nm (0.66 vs. 0.34). However, OC-CCI v4.2 shows lower positive bias values (0.1 10⁻⁴–0.8 10⁻⁴ sr⁻¹ against 1.2 10⁻⁴–6.9 10⁻⁴ sr⁻¹) and performs better in terms of APD or RPD in the 412–560-nm spectral range. Finally, OC-CCI v.6 shows lower bias, APD, and RPD at 665 nm.

The main differences between both OC-CCI versions explaining these discrepancies are the change of reference sensor from SeaWiFS to MERIS, the introduction of OLCI from Sentinel-3A and Sentinel-3B, and the shift of the green band from 555 nm to 560 nm. Moreover, the results in Brando et al. (2021) include 680 match-ups from 2005 to 2019, most of them from Gustav Dalen Tower and Helsinki Lighthouse, as the results from v.6 until 2022 introduce more in-situ data from Irbe Lighthouse. Note also that a stricter quality control (9 valid pixels in the extractions window instead of 4) was introduced in this work.

Table 4 shows the metrics from CMEMS-OLCI Rrs for the three sites. Overall, all of them are characterized by CDOM-dominated waters, and the metrics follow the same spectral pattern. The results from Gustav Dalen Tower and Helsinki Lighthouse lead to similar results, with more match-ups and a better adjustment (R²) from Gustav Dalen Tower. In the case of Irbe Lighthouse, the results show a poorer agreement at 412.5 nm and lower RMSD and bias in the 490–560-nm spectral range, which could be explained by a lower Rrs range.

OC-CCI v.6 chl-a retrievals show validation results (R^{2 =} 0.29; RPD = 5%; APD = 69%; bias = −0.14), consistent with those reported for the previous version of the multisensor level-3 processing chain, i.e., OC-CCI v4.2 (Brando et al., 2021: R^{2 =} 0.24; RPD = 41%; APD = 90%; bias = −0.78), with a better performance considering all the metrics. Overall, the effect of the positive bias observed in the 412–510-nm spectral range (Figures 12, 13) seems to be adequately handled by the ensemble approach.

Chl-a validation results from CMEMS-OLCI are similar to those from OC-CCI v.6 using the common set of match-ups (Figure 13). The most remarkable difference is the lower bias (−0.03 against −0.22), which is consistent with the bias decrease with the introduction of OLCI in the multisensor level-3 datasets from −0.78 (OC-CCI v.4.2, without OLCI, Brando et al., 2021) to −0.22 (OC-CCI v.6, with OLCI, this work). In any case, further research is required because of the small number of chl-a match-ups available for the OLCI period.

Scatter plots of co-collocated OC-CCI v.6 and CMEMS-OLCI data points (Figure 14) show better statistical figures for Rrs (except for 443 nm) than chl-a. Note that OC-CCI v.6 Rrs are derived from different space sensors and AC, whereas CMEMS-OLCI Rrs are obtained from Sentinel-A and Sentinel-B images processed with POLYMER v.4.14. With the exclusion of outliers, which are possibly related to the resolution and coverage of both datasets, most Rrs differences can be attributed to the specificities of data processing and absolute radiometric accuracy of the reference sensor (MERIS for OC-CCI v.6 and OLCI for CMEMS-OLCI).

An overall good agreement was found in the comparison between chl-a retrievals from CMEMS-OLCI and OC-CCI v.6, with a tendency of CMEMS-OLCI toward greater chl-a concentrations (Figure 14).

Nevertheless, the differences in Rrs are amplified in terms of chl-a retrieval. This is probably due to the non-linear nature of the MLP retrievals of chl-a and of the weights in the chl-aENS3 ensemble approach adopted in this study, as well as the underlying relationship between apparent and inherent optical properties in the Baltic Sea.

An issue is the presence of erroneous data points caused by underestimation in the chl-a retrievals from CMEMS-OLCI (<0.5 mg m⁻³) as compared with OC-CCI v.6 (1.5–15 mg m⁻³) (Figure 14). These wrong retrievals are caused by anomalously high OLCI Rrs values with a smaller range at 490 nm (4–5 10⁻³ sr⁻¹ in CMEMS-OLCI against 0.01–3.5 10⁻³ sr⁻¹ in OC-CCI v.6) and 510 nm (3–3.25 10⁻³ sr⁻¹ in CMEMS-OLCI against 0.01–2.5 10⁻³ sr⁻¹ in OC-CCI v.6). Note that these points are not clearly visible in the scatter plots in Figure 14.

Potential differences in the validation results between the available datasets (i.e., COMBINE and Alg@line, see Section 2.2.2), as well as differences in the analytical methods and protocols for the chl-a concentration estimation, were not considered in this study because of the limited number of match-ups, mainly for the OLCI period. It should be noted that Brando et al. (2021) reported higher uncertainty for the match-ups of their multisensor time series with the COMBINE measurements as compared with Alg@line water samples due to different sampling strategies and dynamic ranges of both data sources.

In our sampling to perform the comparative analysis (a point every 10 km, an image every 10 days, see Section 2.5), only 143 points (of 190,546) from 32 images (of 240) were identified as erroneous. For most of the images, only one erroneous point was found, with a maximum of 28 points on a single image. Despite this low impact, further research with a full sampling is required to evaluate the actual effect of these wrong pixels and to implement a flagging procedure.

Regarding wrong Rrs values with a lower impact on the chl-a results (e.g., at 442 nm or 670 nm, see Figure 14), the presence in our sampling is limited to a small number of points by image (often only one). For instance, we identified 216 erroneous points in 64 images at 442 nm and only 32 points in 18 images at 660 nm. In any case, likewise chl-a, a further masking could improve the mapping results.

In this study, the performance of four atmospheric correction processors for the Rrs retrieval from Sentinel-3 OLCI was assessed within the development of the regional ocean color processing chain for the Baltic Sea. The validations with the in-situ measurements collected at three AERONET-OC sites and those relying on the Alg@line shipborne hyperspectral radiometry show that POLYMER v.4.14 was the best-performing processor in terms of error and fitness in the visible spectral range, as well as spatial coverage. Results also document the relevance of shipborne radiometry to complement in-situ measurements from fixed sites, allowing for a larger spatial footprint across all subbasins.

POLYMER-derived Rrs spectra were thus employed to retrieve chl-a from OLCI full-resolution (300 m) data using the bio-optical ensemble scheme already introduced in the CMEMS processing chain for the Baltic Sea. Additionally, this study evaluated the operational Rrs and chl-a multiyear time series (from 1997 to 2022) for the Baltic Sea based on OC-CCI v.6.

The chl-a values retrieved from OC-CCI v.6 and OLCI Rrs using the same regional bio-optical ensemble scheme were compared with the in-situ chl-a measurements. Results confirm previous analyses undertaken within the CMEMS products assessments, even if the number of OLCI match-ups (2016–2019) was lower. A study extension is planned to include more recent in-situ measurements once available.

Finally, an overall good agreement was found in the comparison between chl-a retrievals from OLCI and OC-CCI v.6. However, differences between the Rrs bands used as input for the bio-optical ensemble scheme were amplified in terms of chl-a retrieval. A flagging strategy should be devised to identify and reduce the presence of erroneous data points in both datasets. Furthermore, a sensitivity analysis is then part of the future developments to analyze the response of the bio-optical ensemble by adding synthetic offsets and noise to input Rrs spectra and verify how it affects the chl-a retrieval.

Our results confirm that the quality of operational ocean color datasets presented in this study is suitable for studies on phytoplankton phenology, bloom occurrence, water quality monitoring, and eutrophication assessment in this threatened ecosystem.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: E.U. Copernicus Marine Service Information (CMEMS) Marine Data Store (MDS: marine.copernicus.eu): OCEANCOLOUR_BAL_BGC_L3_NRT_009_131 (Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations). E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00294 (Accessed on 10-JUL-2023)OCEANCOLOUR_BAL_BGC_L3_MY_009_133 (Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations). E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00296 (Accessed on 10-JUL-2023).

LG: Conceptualization, Data curation, Formal analysis, Software, Validation, Visualization, Writing – original draft. VB: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft. ADC: Data curation, Writing – review & editing. SC: Data curation, Software, Writing – review & editing. DD’A: Conceptualization, Funding acquisition, Methodology, Software, Supervision, Writing – review & editing. TK: Writing – review & editing. JA: Data curation, Writing – review & editing. TS: Writing – review & editing.