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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

Monthly CHL-a and Secchi Depth (SD) data derived from the full mission data of the Medium Resolution Imaging Spectrometer (MERIS; 2002–2012) were analysed along a horizontal transect from the inner Bråviken bay and out into the open sea. The CHL-a values were calibrated using an algorithm derived from Swedish lakes. Then, calibrated Chl-a and Secchi Depth (SD) estimates were extracted from MERIS data along the transect and compared to conventional monitoring data as well as to data from the Swedish Coastal zone Model (SCM), providing physico-biogeochemical parameters such as temperature, nutrients, Chlorophyll-a (CHL-a) and Secchi depth (SD). A high negative correlation was observed between satellite-derived CHL-a and SD (ρ = −0.91), similar to the in situ relationship established for several coastal gradients in the Baltic proper. We also demonstrate that the validated MERIS-based estimates and data from the SCM showed strong correlations for the variables CHL-a, SD and total nitrogen (TOTN), which improved significantly when analysed on a monthly basis across basins. The relationship between satellite-derived CHL-a and modelled TOTN was also evaluated on a monthly basis using least-square linear regression models. The predictive power of the models was strong for the period May-November (R2: 0.58–0.87), and the regression algorithm for summer was almost identical to the algorithm generated from in situ data in Himmerfjärden bay. The strong correlation between SD and modelled TOTN confirms that SD is a robust and reliable indicator to evaluate changes in eutrophication in the Baltic proper which can be assessed using remote sensing data. Amongst all three assessed methods, only MERIS CHL-a was able to correctly depict the pattern of phytoplankton phenology that is typical for the Baltic proper. The approach of combining satellite data and physio-biogeochemical models could serve as a powerful tool and value-adding complement to the scarcely available in situ data from national monitoring programs. In particular, satellite data will help to reduce uncertainties in long-term monitoring data due to its improved measurement frequency.

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.

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.

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 ...

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.

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 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.

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.

This chapter focuses on recent advances in water quality monitoring of the Baltic Sea using remote sensing techniques in combination with optical in situ measurements. Here the Baltic Sea ecosystem is observed through its bio-optical properties, which are defined by the concentration of optical in-water constituents governing the spectral attenuation of light. In the introduction, typical geographical patterns and seasonal variations of optical properties and the cause of the mass occurrence of cyanobacterial blooms in summer are discussed. The optical characteristic of Baltic Sea waters is clearly dominated by a relatively high load of dissolved organic matter and, during the productive season, by phytoplankton growth, stimulated by nutrients mostly originating from land. In the coastal zone, inorganic suspended matter also has a significant effect on the light attenuation, which increases with proximity to land. The ecological status of the coastal zone may be synthesized using a bio-optical model, summarizing important ecosystem state variables such as terrestrial runoff and phytoplankton production. The optical properties can also be observed with visible, spectral satellite remote sensing providing repetitive and optically consistent data for the whole Baltic Sea basin. Such observations have already significantly influenced our understanding of Baltic Sea dynamics and provide us with a new look into this brackish ecosystem. The focus in this chapter is on the ocean colour sensor ‘Medium Resolution Imaging Spectrometer’ (MERIS), which since 2002 is flying continuously onboard the European Environmental Satellite ENVISAT, developed by the European Space Agency (ESA). The advantage of MERIS data is its good spatial resolution of 300 m allowing the analysis of coastal features and bays of the Baltic Sea. Algorithms to retrieve bio-optical parameters from MERIS data are continuously improved and extended. The MERIS mission will be continued with the Ocean and Land Colour Instrument (OLCI), an optically similar sensor which will be flown on SENTINEL-3, scheduled to be operational until 2023, to assure long-term monitoring of water quality from space. The wide aerial coverage, the frequent repetition and continuity of the satellite observations, the consistency of the measured data, and a relative cost-effectiveness clearly respond to the demands of a modern operational monitoring system, and the requirements of effective Baltic Sea management. An overview of existing monitoring approaches is given, and operational online systems that combine remote sensing and autonomous in situ measurements are discussed.