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A global in-situ data set for validation of ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in-situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient and total suspended matter. Data were obtained from multi-project archives acquired via open internet services, or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 151,673 rows, with each row representing a unique station in space and time (cf 136,250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68,641 (cf 59,781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.pangaea.de/10.1594/PANGAEA.941318 (Valente et al., 2022)., SI

International audience; Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (̂ρ w ). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in ̂ρ w (560) and ̂ρ w (664) were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15–30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20–30% uncertainties in ̂ρ w (490 ≤ λ ≤ 743 nm) yielded 25–70% uncertainties in derived Chla and TSS products for top-performing AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative perfor­mance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems.

Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA - ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (??w). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. The volume of our data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errorsin ??w(560) and ??w(664) were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15- 30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concertation of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20- 30% uncertainties in ??w(490

Water transparency is one of the ecological indicators for describing water quality and the underwater light field which determines its productivity. In the European Water Framework Directive (WFD) as well as in the European Marine Strategy Framework Directive (MSFD) water transparency is used for ecological status classification of inland, coastal and open sea waters and it is regarded as an indicator for eutrophication in Baltic Sea management (HELCOM, 2007). We developed and compared different empirical and semi-analytical algorithms for lakes and coastal Nordic waters to retrieve Secchi depth (ZSD) from remote sensing data (MERIS, 300 m resolution). The algorithms were developed in water bodies with high coloured dissolved organic matter absorption (aCDOM(442) ranging 1.7–4.0 m−1), Chl a concentration (0.5–73 mg m−3) and total suspended matter (0.7–37.5 g m−3) and validated against an independent data set over inland and coastal waters (0.6 m

In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92–1.00 and MAPD = 1.11%–10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms.

A Water Quality Monitor (WQM) equipped with a range of oceanographic sensors was deployed from April 2017 to October 2017 in the North Western (NW) Baltic Sea. We assessed here if the data from a moored chlorophyll-a fluorometer can be used to improve satellite validation in coastal waters. Calibrated mooring data and ship-based chlorophyll-a concentrations from 2017 and 2018 were matched with OLCI (Ocean and Land Colour Instrument) data to validate the C2RCC (Case-2 Regional Coast Colour) processor, a locally-adapted version of C2RCC (LA-C2RCC), as well as the POLYMER processor. Using additional mooring data resulted in a substantial increase in paired observations compared to using ship-based data alone (C2RCC; N = 41–63, LA-C2RCC; N = 37–59, POLYMER; N = 108–166). However, the addition of mooring data only reduced the error and bias of the LA-C2RCC (MNB: from 24 % to 22 %, RMSE: from 60 % to 57 %, APD: both 47 %). In contrast, the statistical errors increased with the addition of mooring data both for C2RCC (MNB: −26 % to −33 %, RMSE: 50 %–51 %, APD 84 %–96 %) and for POLYMER (MNB: 26 %–36 %, RMSE: 79 % to 79 %, APD 64 %–64 %). The results indicate that the locally-adapted version of the C2RCC should be used for the area of investigation. These results are most likely also related to the effect of the System Vicarious Calibration (SVC). As opposed to C2RCC, the locally-adapted version had not been vicariously calibrated. The results indicate that SVC is not beneficial for Baltic Sea data and that more work needs to be done to improve SVC for Baltic Sea waters or for other waters with high CDOM absorption. In order to improve the validation capabilities of moored fluorometers in general, they should be strategically placed in waters with representative ranges of chl-a concentrations for the area of research in question.

In the present paper, measurements of aerosol optical properties from the Gotland station of the AERONET network, combined with a two-stage cluster analysis of back trajectories of air masses moving over Gotland, were used to identify the main paths of air mass advection to the Baltic Sea and to relate them to aerosol optical properties, i.e. the aerosol optical thickness at the wavelength λ = 500 nm, AOT (500) and the Angstrom exponent for the spectral range from 440 to 870 nm, α(440,870). One- to six-day long back trajectories ending at 300, 500 and 3000 m above the station were computed using the HYSPLIT model. The study shows that in the Gotland region, variability in aerosol optical thickness AOT(500) is more strongly related to advections in the boundary layer than to those in the free troposphere. The observed variability in AOT(500) was best explained by the advection speeds and directions given by clustering of 4-day backward trajectories of air arriving in the boundary layer at 500 m above the station. 17 clusters of 4-day trajectories arriving at altitude 500 m above the Gotland station (sea level) derived using two-stage cluster analysis differ from each other with respect to trajectory length, the speed of air mass movement and the direction of advection. They also show different cluster means of AOT(500) and α(440,870). The cluster mean AOT(500) ranges from 0.342 ± 0.012 for the continental clusters M2 (east-southeast advection with moderate speed) and 0.294 ± 0.025 for S5 (slow south-southeast advection) to 0.064 ± 0.002 and 0.069 ± 0.002 for the respective marine clusters L3 (fast west-northwest advection) and M3 (north-northwest advection with moderate speed). The cluster mean α(440,870) varies from 1.65–1.70 for the short-trajectory clusters to 0.98 ± 0.03 and 1.06 ± 0.03 for the Arctic marine cluster L4 (fast inflow from the north) and marine cluster L5 (fast inflow from the west) respectively.

The partly inaccurate reflectance estimations in combination with both positive and negative bias imply that successful application of band ratio algorithms is unlikely. The high correlation between MERIS FUB water quality products and in situ data, on the other hand, shows a potential to complement present water quality monitoring programmes and improve the understanding and representability of the temporally and spatially sparse in situ observations. The monitoring potential shown in this study is applicable to the Sentinel-3 mission’s OLCI Ocean Land Colour Instrument, which was launched by the European Space Agency ESA in February 2016 as a part of the EC Copernicus programme.

Earth Observation (EO) is a cost-effective tool for obtaining spatial information on the water quality status and processes taking place in seas and lakes. In particular, recent and expected advances in EO technology broaden the perspectives of monitoring toward the identification and quantification of plankton groups including cyanobacteria. The capability to detect cyanobacteria bloom is important because they can develop toxins hence presenting a range of amenity, water quality and treatment problems, as well as hazards to human health and ecological equilibrium. The problem is especially acute in the Baltic Sea where cyanobacterial blooms occur every summer covering areas of more than 100.000 km2 and washing upon beaches during the summer holiday season. With respect to lakes, they frequently exhibit cyanobacterial blooms, with an increasing occurrence presumably in correlation with eutrophication and climate change, which has resulted in economic loss and considerable public interest in this phenomenon. In such a frame, CYAN IS-WAS, aims to develop regional algorithms for detecting cyanobacterial blooms from both satellite and airborne data, and thence use those algorithms for mapping cyanobacteria during time, and thence try to derive the main environmental/ecological variables which cause the development of cyanobacteria. The cooperation activities are carried out by sharing EO data, methods and by performing joint field campaigns. The project involves training of PhD and MSc students of both countries for giving them experience in EO data analysis. The project is divulgated throughout a series of events, including scientific symposium/workshop and with scientific publications in ISI journals. The exploitation of EO data allowed us to identify the occurrence, blooming, persistence and degradation of cyanobacteria communities in selected Swedish and Italian aquatic ecosystems. The main scientific findings are:  an extremely high variability in cyanobacteria optical properties was found, thus enhancing the great importance of tuning the EO based algorithms depending on study area features;  hyperspectral images, appropriately corrected, calibrated and validated, enabled the mapping of cyanobacteria blooms; some advantage of using radar data was also demonstrated for mapping abundant blooms in presence of clouds;  comparative studies on marine (Baltic vs. north Adriatic) and freshwater (Vänern vs. subalpine deep lakes, Trasimeno and Mantova) have been accomplished thanks to joint field campaign and satellite images analysis;  Nitrogen limitation in summer in the Baltic Sea proper is a decisive factor contributing to the development of blooms of filamentous cyanobacteria occurring during summer; EO work in the NW Baltic Sea has shown that we can derive chlorophyll concentrations in Baltic Sea coastal waters reliably from MERIS data (Hervey et al. 2013);  the amount of suspended solids transported by Po river (but also by other secondary rivers as Adige and Tagliamento) into the north Adriatic Sea, reduces the available light that can be used by phytoplankton component hence also hindering the growth of cyanobacteria

Phytoplankton and heterotrophic bacteria are key groups at the base of aquatic food webs. In estuaries receiving riverine water with a high content of coloured allochthonous dissolved organic matter (ADOM), phytoplankton primary production may be reduced, while bacterial production is favoured. We tested this hypothesis by performing a field study in a northerly estuary receiving nutrient-poor, ADOM-rich riverine water, and analyzing results using multivariate statistics. Throughout the productive season, and especially during the spring river flush, the production and growth rate of heterotrophic bacteria were stimulated by the riverine inflow of dissolved organic carbon (DOC). In contrast, primary production and photosynthetic efficiency (i.e. phytoplankton growth rate) were negatively affected by DOC. Primary production related positively to phosphorus, which is the limiting nutrient in the area. In the upper estuary where DOC concentrations were the highest, the heterotrophic bacterial production constituted almost 100% of the basal production (sum of primary and bacterial production) during spring, while during summer the primary and bacterial production were approximately equal. Our study shows that riverine DOC had a strong negative influence on coastal phytoplankton production, likely due to light attenuation. On the other hand DOC showed a positive influence on bacterial production since it represents a supplementary food source. Thus, in boreal regions where climate change will cause increased river inflow to coastal waters, the balance between phytoplankton and bacterial production is likely to be changed, favouring bacteria. The pelagic food web structure and overall productivity will in turn be altered. Fil: Andersson, Agneta. Universidad de Umea; Suecia. Umeå Marine Sciences Centre; Suecia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Brugel, S.. Universidad de Umea; Suecia. Umeå Marine Sciences Centre; Suecia Fil: Paczkowska, Joanna Marianna. Universidad de Umea; Suecia. Umeå Marine Sciences Centre; Suecia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Rowe, O.F.. Universidad de Umea; Suecia. Umeå Marine Sciences Centre; Suecia. University of Helsinki; Finlandia Fil: Figueroa, D.. Universidad de Umea; Suecia. Umeå Marine Sciences Centre; Suecia Fil: Kratzer, S.. Stockholms Universitet; Suecia Fil: Legrand, C.. Linnaeus University; Suecia

In order to retrieve geophysical satellite products in coastal waters with high coloured dissolved organic matter (CDOM), models and processors require parameterization with regional specific inherent optical properties (sIOPs). The sIOPs of the Baltic Sea were evaluated and compared to a global NOMAD/COLORS Reference Data Set (RDS), covering a wide range of optical provinces. Ternary plots of relative absorption at 442 nm showed CDOM dominance over phytoplankton and non-algal particle absorption (NAP). At 670 nm, the distribution of Baltic measurements was not different from case 1 waters and the retrieval of Chl a was shown to be improved by red-ratio algorithms. For correct retrieval of CDOM from Medium Resolution Imaging Spectrometer (MERIS) data, a different CDOM slope over the Baltic region is required. The CDOM absorption slope, SCDOM, was significantly higher in the northwestern Baltic Sea: 0.018 (±0.002) compared to 0.016 (±0.005) for the RDS. Chl a-specific absorption and ad [SPM]*(442) and its spectral slope did not differ significantly. The comparison to the MERIS Reference Model Document (RMD) showed that the SNAP slope was generally much higher (0.011 ± 0.003) than in the RMD (0.0072 ± 0.00108), and that the SPM scattering slope was also higher (0.547 ± 0.188) vs. 0.4. The SPM-specific scattering was much higher (1.016 ± 0.326 m2 g−1) vs. 0.578 m2 g−1 in RMD. SPM retrieval could be improved by applying the local specific scattering. A novel method was implemented to derive the phase function (PF) from AC9 and VSF-3 data. b ˜ was calculated fitting a Fournier–Forand PF to the normalized VSF data. b ˜ was similar to Petzold, but the PF differed in the backwards direction. Some of the sIOPs showed a bimodal distribution, indicating different water types—e.g., coastal vs. open sea. This seems to be partially caused by the distribution of inorganic particles that fall out relatively close to the coast. In order to improve remote sensing retrieval from Baltic Sea data, one should apply different parameterization to these distinct water types, i.e., inner coastal waters that are more influenced by scattering of inorganic particles vs. open sea waters that are optically dominated by CDOM absorption.

In this study, retrievals of the medium resolution imaging spectrometer (MERIS) reflectances and water quality products using four different coastal processing algorithms freely available are assessed by comparison against sea-truthing data. The study is based on a pair-wise comparison using processor-dependent quality flags for the retrieval of valid common macro-pixels. This assessment is required in order to ensure the reliability of monitoring systems based on MERIS data, such as the Swedish coastal and lake monitoring system (http://vattenkvalitet.se). The results show that the pre-processing with the Improved Contrast between Ocean and Land (ICOL) processor, correcting for adjacency effects, improves the retrieval of spectral reflectance for all processors. Therefore, it is recommended that the ICOL processor should be applied when Baltic coastal waters are investigated. Chlorophyll was retrieved best using the FUB (Free University of Berlin) processing algorithm, although overestimations in the range 18–26.5%, dependent on the compared pairs, were obtained. At low chlorophyll concentrations (< 2.5 mg m−3), data dispersion dominated in the retrievals with the MEGS (MERIS ground segment processor) processor. The lowest bias and data dispersion were obtained with MEGS for suspended particulate matter, for which overestimations in the range of 8–16% were found. Only the FUB retrieved CDOM (coloured dissolved organic matter) correlate with in situ values. However, a large systematic underestimation appears in the estimates that nevertheless may be corrected for by using a local correction factor. The MEGS has the potential to be used as an operational processing algorithm for the Himmerfjärden bay and adjacent areas, but it requires further improvement of the atmospheric correction for the blue bands and better definition at relatively low chlorophyll concentrations in the presence of high CDOM attenuation.

In this study, empirical and semianalytical algorithms are developed and compared for optically complex waters to retrieve the diffuse attenuation coefficient of downwelling irradiance (Kd(lambda)) from satellite data. In the first approach, a band ratio algorithm was used. Various sets of MERIS band ratios were tested to achieve the best estimates for K-d(490) based on the in situ dataset which was measured in Nordic lakes (oligotrophic to eutrophic conditions). In the second approach, K-d(490) was expressed as a function of inherent optical properties which were retrieved from MERIS standard products. The algorithms from both approaches were tested against an independent data set and validated in optically complex coastal waters in the Baltic Sea and in Nordic lakes with high concentrations of coloured dissolved organic matter (0.3 2.5 m(-1)). As a result, a combined band ratio algorithm was developed, which provides a promising approach R-2 = 0.98, RMSE= 17%, N = 34, p < 0.05) for estimating K-d(490) over a wide range of values (0.3-6.1 m(-1)).

The coastal zones are the most inhabited areas of the world and are therefore strongly affected by humans, leading to undesirable environmental changes that may alter the ecosystems, such as eutrop ...

Eutrophication is an increasing problem in coastal waters of the Baltic Sea. Moreover, algal blooms, which occur every summer in the Gulf of Gdansk can deleteriously impact human health, the aquatic environment, and economically important fisheries, tourism, and recreation industries. Traditional laboratory-based techniques for water monitoring are expensive and time consuming, which usually results in limited numbers of observations and discontinuity in space and time. The use of hyperspectral radiometers for coastal water observation provides the potential for more detailed remote optical monitoring. A statistical approach to develop local models for the estimation of optically significant components from in situ measured hyperspectral remote sensing reflectance in case 2 waters is presented in this study. The models, which are based on empirical orthogonal function (EOF) analysis and stepwise multilinear regression, allow for the estimation of parameters strongly correlated with phytoplankton (pigment concentration, absorption coefficient) and coloured detrital matter abundance (absorption coefficient) directly from reflectance spectra measured in situ. Chlorophyll a concentration, which is commonly used as a proxy for phytoplankton biomass, was retrieved with low error (median percent difference, MPD = 17%, root mean square error RMSE = 0.14 in log10 space) and showed a high correlation with chlorophyll a measured in situ (R = 0.84). Furthermore, phycocyanin and phycoerythrin, both characteristic pigments for cyanobacteria species, were also retrieved reliably from reflectance with MPD = 23%, RMSE = 0.23, R2 = 0.77 and MPD = 24%, RMSE = 0.15, R2 = 0.74, respectively. The EOF technique proved to be accurate in the derivation of the absorption spectra of phytoplankton and coloured detrital matter (CDM), with R2 (λ) above 0.83 and RMSE around 0.10. The approach was also applied to satellite multispectral remote sensing reflectance data, thus allowing for improved temporal and spatial resolution compared with the in situ measurements. The EOF method tested on simulated Medium Resolution Imaging Spectrometer (MERIS) or Ocean and Land Colour Instrument (OLCI) data resulted in RMSE = 0.16 for chl-a and RMSE = 0.29 for phycocyanin. The presented methods, applied to both in situ and satellite data, provide a powerful tool for coastal monitoring and management.

1. The colour of the sea, i.e. its spectral reflectance, depends on the absorbing and scattering properties of substances in the water. 2. The main optical in-water constituents are chlorophyll a (Chl a), coloured dissolved organic matter (CDOM) and suspended particulate matter (SPM). 3. Optical data can be obtained from sensors deployed into the water or by remote sensing imagers on aircrafts or satellites. 4. With remote sensing, the optical properties of large geographical areas can be surveyed with high temporal and spatial resolution. 5. Chl a can be used as a proxy of phytoplankton biomass, CDOM as a marker of terrestrial freshwater and decay processes of marine primary producers and SPM as an indicator of land runoff and wind-driven resuspension of sediments. 6. Remote sensing of Chl a, CDOM and SPM can assist in the evaluation of water quality, e.g. the state of eutrophication, the extent of freshwater runoff, the depth of the photic zone and the breadth of the coastal zone. 7. The bio-optical characteristics of the brackish Baltic Sea differ from those of other seas. Due to the large overall freshwater influence, CDOM is usually the dominant optical in-water constituent not only near river discharges, but also in the open waters of the Baltic Sea. 8. The CDOM concentrations in the open waters of the Baltic Sea are inversely related to the large-scale Baltic Sea salinity gradient, with CDOM absorption highest in the northern Baltic Sea and lowest in the southwestern Baltic Sea. 9. Due to the high CDOM absorption regional Baltic Sea algorithms are required to derive water quality parameters that can be used as indicators of ecosystem health.

In this study the use of ocean color data as a diagnostic tool in integrated coastal zone management was investigated as part of the Science Policy Integration for Coastal Systems Assessment (SPICOSA) project. Parallel to this, an operational coastal monitoring system has been set up in close collaboration with end-users. The core work of the bio-optical part in the project was to develop Secchi depth and attenuation of light as indicators for coastal zone management, by linking remote sensing with the socio-economic and ecological model developed in SPICOSA. The article emphasizes the benefits of stakeholder involvement and end-user feedback for efficient and improved system development. Furthermore, conceptual models were developed on how to integrate remote sensing data into coastal zone management and into a physical-biological model of the Baltic Sea. One of the work packages in the SPICOSA project was academic training. In this work package, on-line teaching material in the field of remote sensing and bio-optics was developed and disseminated on the SETnet web page. The article presented here may act as supportive material for training in bio-optics and remote sensing.

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS), and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales, their role in marine biogeochemistry, the global carbon cycle, the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean, and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration, since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency, the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA), and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi:10.1594/PANGAEA.854832 (Valente et al., 2015).

In this chapter we address some of the recent developments in marine coastal remote sensing with regards to the evaluation of water quality from space using multi-temporal data. Most chapters in this book are devoted to terrestrial applications, whereas aquatic remote sensing requires a completely different approach in terms of mission and sensor design as well as data analysis and processing. Therefore, the first section is a general introduction to marine remote sensing. Then we report recent results from remote sensing of the Baltic Sea, which is optically dominated by the absorption of light by coloured dissolved organic matter (CDOM), and during summer months, by high standing stocks of filamentous cyanobacteria. Results both from basin-wide as well as coastal applications in the north-western Baltic Sea are presented. In next section we report results from the Bay of Biscay in the north-eastern Atlantic Ocean west of France, which is an area highly influenced by river discharge and dinoflagellate blooms, and the subsequent section is about a coastal area in the eastern Beaufort Sea in the Arctic that’s influenced by a pool of CDOM. In all sections we discuss the relevance of regional remote sensing for ecological analysis and coastal management. The chapter concludes with a synthesis on merging of satellite data from different ocean colour missions and the limitations for coastal applications are discussed.

This paper presents the results of the studies of aerosol optical properties measured using lidars and sun photometers. We describe two case studies of the combined measurements made in two coastal zones in Crete in 2006 and in Rozewie on the Baltic Sea in 2009. The combination of lidar and sun photometer measurements provides comprehensive information on both the total aerosol optical thickness in the entire atmosphere as well as the vertical structure of aerosol optical properties. Combination of such information with air mass back-trajectories and data collected at stations located on the route of air masses provides complete picture of the aerosol variations in the study area both vertically and horizontally. We show that such combined studies are especially important in the coastal areas where depending on air mass advection directions and altitudes the influence of fine or coarse mode (in this case possibly sea-salt) particles on the vertical structure of aerosol optical properties is an important issue to consider.

In order to develop an optical model to map the extent of coastal waters, the authors analyzed variations in bio-optical constituents and submarine optical properties along a transect from the nutrient-enriched coastal bay, Himmerfjarden, out into the open Baltic Sea. The model is a simple implementation of the “ecosystem approach,” because the optical constituents are proxies for important components of ecosystem state. Yellow substance or colored dissolved organic matter (CDOM) is often a marker for terrestrial freshwater or decay processes in the littoral zone. Phytoplankton pigments, especially chlorophyll a, are used as a proxy for phytoplankton biomass that may be stimulated by fluvial or coastal inputs of anthropogenic nutrients. Suspended particulate matter (SPM) is placed in suspension by tidal or wind-wave stirring of shallow seabeds, and is therefore an indicator for physical forcing. It is the thesis of this article that such constituents, and the optical properties that they control, can be used to provide an ecological definition of the extent of the coastal zone. The spatial distribution of the observations was analyzed using a steady-state model that assumes diffusional transport of bio-optical variables along an axis perpendicular to the coast. According to the model, the resulting distribution along this axis can be described as a low-order polynomial (of order 1–3) when moving from a “source” associated with land to the open-sea “sink.” Order 1 implies conservative mixing, and the higher orders imply significant biological or chemical processes within the gradient. The analysis of the transect data confirmed that the trend of each optical component could be described well using a low-order polynomial. Multiple regression analysis was then used to weigh the contribution of each optical component to the spectral attenuation coefficient K d(490) along the transect. The results showed that in this Swedish Baltic case study, the inorganic fraction of the SPM may be used to distinguish between coastal and open-sea waters, as it showed a clear break between coastal and open-sea waters. Alternative models may be needed for coastal waters in which fronts interrupt the continuity of mixing.

In this paper we investigate if MERIS full resolution (FR) data (300 m) is sufficient to monitor changes in optical constituents in Himmerfjarden, a fjord-like, north– south facing bay of about 30 ...

Due to high terrestrial runoff, the Baltic Sea is rich in dissolved organic carbon (DOC), the light-absorbing fraction of which is referred to as colored dissolved organic matter (CDOM). Inputs of DOC and CDOM are predicted to increase with climate change, affecting coastal ecosystems. We found that the relationships between DOC, CDOM, salinity, and Secchi depth all differed between the two coastal areas studied; the W Gulf of Bothnia with high terrestrial input and the NW Baltic Proper with relatively little terrestrial input. The CDOM:DOC ratio was higher in the Gulf of Bothnia, where CDOM had a greater influence on the Secchi depth, which is used as an indicator of eutrophication and hence important for Baltic Sea management. Based on the results of this study, we recommend regular CDOM measurements in monitoring programmes, to increase the value of concurrent Secchi depth measurements. Electronic supplementary material The online version of this article (doi:10.1007/s13280-015-0658-4) contains supplementary material, which is available to authorized users.

The launch of several new satellites such as Sentinel-2, Sentinel-3, HyspIRI, EnMAP and PRISMA in the very near future, opens new perspectives for the inland and coastal water community. The monitoring of the water quality closer to the coast, within estuaries or small lakes with satellite data will become feasible. However for these inland and nearshore coastal waters, adjacency effects may hamper the correct retrieval of water quality parameters from remotely sensed imagery. Here, we present a sensor-generic adjacency pre-processing method, SIMilarity Environment Correction (SIMEC). The correction algorithm estimates the contribution of the background radiance based on the correspondence with the Near-INfrared (NIR) similarity spectrum. The performance of SIMEC was tested on MERIS FR images both above highly reflecting waters with high SPM loads, as well as dark lake waters with high CDOM absorption. The results show that SIMEC has a positive or neutral effect on the normalized remote sensing reflectance above optically-complex waters, retrieved with the MERIS MEGS or C2R processor.

Regions of Restricted Exchange (RREs) are an important feature of the European coastline. They are historically preferred sites for human settlement and aquaculture and their ecosystems, and consequent human use, may be at risk from eutrophication. The OAERRE project (EVK3-CT1999-0002) concerns 'Oceanographic Applications to Eutrophication in Regions of Restricted Exchange'. It began in July 2000, and studies six sites. Four of these sites are fjords: Kongsfjorden (west coast of Spitzbergen); Gullmaren (Skagerrak coast of Sweden); Himmerfjarden (Baltic coast of Sweden); and the Firth of Clyde (west coast of Scotland). Two are bays sheltered by sand bars: Golfe de Fos (French Mediterranean); and Ria Formosa (Portuguese Algarve). Together they exemplify a range of hydrographic and enrichment conditions. The project aims to understand the physical, biogeochemical and biological processes, and their interactions, that determine the trophic status of these coastal marine RRE through the development of simple screening models to define, predict and assess eutrophication. This paper introduces the sites and describes the component parts of a basic screening model and its application to each site using historical data. The model forms the starting point for the OAERRE project and views an RRE as a well-mixed box, exchanging with the sea at a daily rate E determined by physical processes, and converting nutrient to phytoplankton chlorophyll at a fixed yield q. It thus uses nutrient levels to estimate maximum biomass; these preliminary results are discussed in relation to objective criteria used to assess trophic status. The influence of factors such as grazing and vertical mixing on key parameters in the screening model are further studied using simulations of a complex 'research' model for the Firth of Clyde. The future development of screening models in general and within OAERRE in particular is discussed. In addition, the paper looks ahead with a broad discussion of progress in the scientific understanding of eutrophication and the legal and socioeconomic issues that need to be taken into account in managing the trophic status of RREs.