Chlorophyll Dynamics from Sentinel-3 Using an Optimized Algorithm for Enhanced Ecological Monitoring in Complex Urban Estuarine Waters

Urban estuaries are dynamic environments that hold high ecological and economic value. Yet, their optical complexity hinders accurate satellite retrievals of important biogeochemical variables, such as chlorophyll-a (Chl-a) biomass. Approaches based on a limited number of satellite spectral bands often fail to capture seasonal transitions and sharp spatial gradients in estuarine Chl-a concentrations, inhibiting integration of satellite data into water quality monitoring and conservation programs. We propose a novel approach that utilizes the wide range of spectral information captured by the Ocean and Land Color Instrument (OLCI) to retrieve estuarine Chl-a. To validate our approach, we used measurements in Long Island Sound (LIS), a highly urbanized estuary increasingly susceptible to anthropogenic stressors and climate change. Hyperspectral remote sensing reflectance (Rrs) and Chl-a data representing the spatiotemporal diversity of LIS were used to assess the ideal atmospheric correction approach for OLCI and develop a multi-spectral multiple linear regression (MS-MLR) Chl-a algorithm. POLYMER derived Rrs proved to be the preferred atmospheric correction approach. Evaluation of MS-MLR performance in retrieving Chl-a with in situ Rrs showed good agreement with field measurements. Application to OLCI-retrieved Rrs showed significant improvement (20%-30%) in common error metrics relative to other algorithms assessed. The MS-MLR approach successfully captured seasonal cycles and spatial gradients in Chl-a concentration. Application of this method to urban estuaries and coasts enables accurate, high resolution Chl-a observations at the ecosystem scale and across a range of conditions, as needed for conservation and ecosystem management efforts

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions

Seasonal dynamics of dissolved organic matter in the Mackenzie Delta, Canadian Arctic waters

Increasing air temperatures and associated permafrost thaw in Arctic river watersheds, such as the Mackenzie River catchment, are directly affecting the aquatic environment. As a consequence, the quantity and the quality of dissolved organic carbon (DOC) that is transported via the Mackenzie River into the Arctic Ocean is expected to change. Particularly in these remote permafrost regions of the Arctic, monitoring of terrigenous organic carbon fluxes is insufficient and knowledge of distribution and fate of organic carbon when released to the coastal waters is remarkably lacking. Despite its poorly evaluated performance in Arctic coastal waters, Satellite Ocean Colour Remote Sensing (SOCRS) remains a powerful tool to complement monitoring of land-ocean DOC fluxes, detect their trends, and help in understanding their propagation in the Arctic Ocean. In this study, we use in situ and SOCRS data to show the strong seasonal dynamics of the Mackenzie River plume and the spatial distribution of associated terrigenous DOC on the Beaufort Sea Shelf for the first time. Using a dataset collected during an extensive field campaign in 2019, the performance of three commonly-used atmospheric correction (AC) algorithms and two available colored dissolved organic matter (CDOM) retrieval algorithms were evaluated using the Ocean and Land Colour Instrument (OLCI). Our results showed that in optically-complex Arctic coastal waters the Polymer AC algorithm performed the best. For the retrieval of CDOM, the gsmA algorithm (Mean Percentage Error (MPE) = 35.7%) showed slightly more consistent results compared to the ONNS algorithm (MPE = 37.9%). By merging our measurements with published datasets, the newly-established DOC-CDOM relationship for the Mackenzie-Beaufort Sea region allowed estimations of DOC concentrations from SOCRS across the entire fluvial-marine transition zone with an MPE of 20.5%. Finally, we applied SOCRS with data from the Sentinel-3 OLCI sensor to illustrate the seasonal variation of DOC concentrations in the surface waters of the Beaufort Sea on a large spatial scales and high frequency throughout the entire open water period. Highest DOC concentrations and largest lateral extent of the plume were observed in spring right after the Mackenzie River ice break-up indicating that the freshet was the main driver of plume propagation and DOC distribution on the shelf. Satellite-derived images of surface water DOC concentration placed the in situ observations into a larger temporal and spatial context and revealed a strong seasonal variability in transport pathways of DOC in the Mackenzie- Beaufort Sea region

Sensitivity of remotely sensed pigment concentration via Mixture Density Networks (MDNs) to uncertainties from atmospheric correction

Lake Erie, the shallowest of the five North American Laurentian Great Lakes, exhibits degraded water quality associated with recurrent phytoplankton blooms. Optical remote sensing of these optically com�plex inland waters is challenging due to the uncertainties stemming from atmospheric correction (AC) procedures. In this study, the accuracy of remote sensing reflectance (Rrs) derived from three different AC algorithms applied to Ocean and Land Colour Instrument (OLCI) observations of western Lake Erie (WLE) is evaluated through comparison to a regional radiometric dataset. The effects of uncertainties in Rrs products on the retrieval of near-surface concentration of pigments, including chlorophyll-a (Chla) and phycocyanin (PC), from Mixture Density Networks (MDNs) are subsequently investigated. Results show that iCOR contained the fewest number of processed (unflagged) days per pixel, compared to ACOLITE and POLYMER, for parts of the lake. Limiting results to the matchup dataset in common between the three AC algorithms shows that iCOR and ACOLITE performed closely at 665 nm, while out�performing POLYMER, with the Median Symmetric Accuracy (MdSA) of �30 %, 28 %, and 53 %, respec�tively. MDN applied to iCOR- and ACOLITE-corrected data (MdSA < 37 %) outperformed MDN applied to POLYMER-corrected data in estimating Chla. Large uncertainties in satellite-derived Rrs propagated to uncertainties �100 % in PC estimates, although the model was able to recover concentrations along the 1:1 line. Despite the need for improvements in its cloud-masking scheme, we conclude that iCOR combined with MDNs produces adequate OLCI pigment products for studying and monitoring Chla across WL

A New Retrieval of Sun-Induced Chlorophyll Fluorescence in Water from Ocean Colour Measurements Applied on OLCI L-1b and L-2

The retrieval of sun-induced chlorophyll fluorescence is greatly beneficial to studies of marine phytoplankton biomass, physiology, and composition, and is required for user applications and services. Customarily phytoplankton chlorophyll fluorescence is determined from satellite measurements through a fluorescence line-height algorithm using three bands around 680 nm. We propose here a modified retrieval, making use of all available bands in the relevant wavelength range, with the goal to improve the effectiveness of the algorithm in optically complex waters. For the Ocean and Land Colour Instrument (OLCI), we quantify a Fluorescence Peak Height by fitting a Gaussian function and related terms to the top-of-atmosphere reflectance bands between 650 and 750 nm. This algorithm retrieves, what we call Fluorescence Peak Height by fitting a Gaussian function upon other terms to top-of-atmosphere reflectance bands between 650 and 750 nm. This approach is applicable to Level-1 and Level-2 data. We find a good correlation of the retrieved fluorescence product to global in-situ chlorophyll measurements, as well as a consistent relation between chlorophyll concentration and fluorescence from radiative transfer modelling and OLCI/in-situ comparison. Evidence suggests, the algorithm is applicable to complex waters without needing an atmospheric correction and vicarious calibration, and features an inherent correction of small spectral shifts, as required for OLCI measurements

Remote sensing for water quality studies: test of Suspended Particulate Matter and turbidity algorithms for Portuguese transitional and inland waters

Tese de mestrado em Ciências do Mar, Universidade de Lisboa, Faculdade de Ciências, 2020As partículas em Suspensão (SPM) é um dos principais constituintes da água nos estuários e, juntamente com a turbidez (T), é um parâmetro chave para a avaliação da qualidade da água. Através da absorção e difusão da luz, a concentração de SPM reduz a penetração da irradiância solar na coluna de água e limita a radiação fotossinteticamente disponível (PAR) para os produtores primários. Uma vez que a turbidez é altamente correlacionada com a concentração de SPM, para fins de monitorização da qualidade da água, a turbidez é listada como parâmetro obrigatório a ser medido pelos estados membros da União Europeia na Diretiva-Quadro Estratégia Marinha. Portanto, a quantificação destes dois parâmetros, a sua distribuição geográfica e o modo como se relacionam são de interesse crucial para o estudo dos ecossistemas, assim como para a investigação de diferentes processos, como transporte de sedimentos, produção primária e funcionamento de comunidades bentónicas. A monitorização dos parâmetros da qualidade da água, é geralmente alcançado através de programas de amostragem in situ. No entanto, a realização regular de amostragens exige trabalho intensivo e é dispendioso. Além disso, é necessário assumir que as amostras analisadas, que estão limitadas em termos espaciais e temporais, são representativas da área total de interesse. Neste âmbito, a deteção remota da cor do oceano é uma ferramenta eficiente para monitorizar os parâmetros da qualidade da água. O crescente interesse em entender o potencial desta técnica é impulsionado pelos custos reduzidos e pela alta resolução espacial que permite obter resultados para grandes áreas, mas também pela grande frequência temporal dos dados. No entanto, a complexidade das águas costeiras, transitórias e interiores dificulta a deteção das variáveis de interesse devido à proximidade da terra e aos elevados níveis de reflectância causados pela alta concentração de SPM nas regiões espectrais do visível e infravermelho próximo. Não obstante, algoritmos têm vindo a ser desenvolvidos para estimar a concentração de SPM e turbidez, que são geralmente calibrados regionalmente para as características óticas dos diferentes locais. Neste contexto, a presente dissertação teve como foco o teste de diferentes algoritmos com aplicabilidade global para estimar o SPM e a turbidez, bem como a avaliação de diferentes modelos de correção atmosférica. O principal objetivo deste trabalho foi determinar o esquema de processamento mais apropriado para quantificar o SPM e a turbidez em águas de transição e interiores em Portugal, determinando as incertezas associadas aos algoritmos de aplicabilidade global (Nechad et. al. (2009) para o SPM e Dogliotti et. al. (2015) para a turbidez) quando aplicados fora da sua região de calibração. Para este fim, o estuário do Tejo e do Sado e cinco albufeiras na região do Alentejo em Portugal, foram utilizados como casos de estudo para testar o uso de imagens de satélite na monitorização da turbidez e SPM. A base de dados in situ foi adquirida no contexto de diferentes projetos (PLATAGUS, NIPOGES, Valor Sul, AQUASado, GAMEFISH) entre julho de 2017 e julho de 2019, dependendo do projeto. Os dados de satélite testados foram obtidos pelos Sentinel-2 MultiSpectral Instrument (S2-MSI) e o Sentinel-3 Ocean and Land Colour Instrument (S3-OLCI), missões do programa de Observação da Terra da Comissão Europeia - Copernicus. No estuário do Tejo, as medições radiométricas in situ realizadas no contexto do projeto PLATAGUS permitiram também testar diretamente diferentes processadores para a correção atmosférica, nomeadamente o Acolite (S2-MSI), C2RCC (S2-MSI e S3-OLCI), L2 padrão MSI (Sen2Cor), L2 padrão OLCI (BAC / BPAC) e Polymer (S2-MSI e S3-OLCI). Tendo-se obtido melhores resultados com o Polymer e o C2RCC utilizando dados do S2-MSI, e resultados inconclusivos na avaliação dos dados com o S3-OLCI devido ao reduzido número de dados disponíveis. Na avaliação dos algoritmos de SPM e turbidez, os resultados obtidos sugerem que o produto de turbidez é mais fácil de estimar com menores incertezas associadas. Em relação à estimativa do SPM através dos dados S2-MSI e S3-OLCI, as correlações e erros associados indicam que ainda há uma forte necessidade de desenvolvimento de novos algoritmos, com uma calibração regional específica para as características óticas das áreas de estudo ou para encontrar uma relação local entre SPM e turbidez, como já sugerido anteriormente na literatura. Além disso, o sensor S3-OLCI, que apresentou resultados satisfatórios para o estuário do Tejo, mostrou resultados discordantes para o estuário do Sado, sugerindo uma menor adequação da resolução espacial do OLCI (300 m) para estuários de menor dimensão. No território português, as técnicas de deteção remota para monitorização da qualidade da água já estão em uso, mas têm sido testadas e aplicadas principalmente em águas costeiras. Este trabalho é um primeiro esforço para validar produtos de qualidade da água em águas de transição e interiores em Portugal. A importância destes ecossistemas, assim como o papel crucial da validação de produtos de deteção remota para monitorização ambiental e a principal motivação deste trabalho, e determinantes na definição das principais questões abordadas.Suspended particulate matter (SPM) is one of the main water constituents in estuaries and along with turbidity (T), which is highly correlated with SPM concentration, are key parameters to evaluate water quality. Through light absorption and scattering, the SPM concentration reduces the penetration of solar irradiance within the water column and limits the photosynthetically available radiation (PAR) for primary producers making it a relevant indicator for water quality monitoring. In fact, regarding water quality monitoring, turbidity is listed as a mandatory parameter to be measured by EU member states in the Marine Strategy Framework Directive. Therefore, the quantification of these two parameters, their geographical distribution and their relationship are of crucial interest for ecosystems studies and to understand different processes such as sediment transport, primary production and the functioning of benthic communities. Monitoring water quality parameters is usually achieved through field sampling programs. However, conducting regular field sampling is labor intensive and expensive and it is often necessary to assume that field samples, which are limited both spatially and temporally, are representative of the total area of interest. Satellite Ocean Colour Remote Sensing is an efficient tool to monitor these two parameters and the incrementing interest on understanding the potential of this technique is driven by the reduced costs and the high spatial and temporal resolution that allows obtaining results for large areas. However, remote sensing of coastal, transitional and inland waters is a complicated issue due to the proximity of the land and the high levels of reflectance caused by high SPM concentration in the visible and near infrared spectral regions. Many algorithms to retrieve SPM and T already exist and are often calibrated regionally for the optical characteristics of the different sites. In this context, this thesis focuses on the test of different algorithms with global applicability for SPM and turbidity retrieval, as well as different atmospheric corrections. The main aim of the present work is to determine the most appropriate processing scheme to retrieve SPM and turbidity for Portuguese transitional and inland waters and to determine the accuracy of retrieval algorithms with global applicability (Nechad et. al, 2009 for SPM retrieval and Dogliotti et. al., 2015 for turbidity) outside their calibration region. For this purpose, Tagus and Sado estuary, and five small water reservoirs in the Alentejo region in Portugal have been used as case-studies to test satellite imagery capability to monitor SPM and turbidity products. The in situ data for reference has been collected within the context of different projects (PLATAGUS, NIPOGES, Valor Sul, AQUASado, GAMEFISH) from July 2017 to July 2019 depending on the project. The satellite data used were obtained from the Sentinel-2 MultiSpectral Instrument (S2- MSI) and the Sentinel-3 Ocean and Land Colour Instrument. (S3-OLCI), missions from the European Commission Earth Observation program, Copernicus. In the Tagus estuary, in situ radiometric measurements conducted within the context of the PLATAGUS project allowed also to directly test different atmospheric corrections processors, namely (S2-MSI), C2RCC (S2-MSI and S3-OLCI), L2 standard MSI (Sen2Cor), L2 standard OLCI (BAC/BPAC) and Polymer (S2-MSI and S3-OLCI). Being Polymer and C2RCC the best performing algorithms for S2- MSI, while no definite results was found for S3-OLCI given the low number available data. Results suggested that turbidity is easier to retrieve with smaller uncertainties associated. Regarding the SPM retrieval from S2-MSI and S3-OLCI data, the associated correlations and errors indicate that there is still a strong need of algorithms development perhaps with a regional calibration specific for the optical characteristics of the study areas or finding a local relationship between SPM and turbidity as has been previously suggested. Moreover, the S3-OLCI sensor, which gave satisfactory results for the Tagus estuary, showed discordant results for the Sado estuary suggesting a poor suitability of the OLCI spatial resolution (300m) for smaller estuaries. In the Portuguese territory, remote sensing techniques have been tested and are in place for water quality monitoring mostly for coastal application. This work is a first effort to validate satellite-derived water quality products for monitoring transitional and inland waters in Portugal. The well-known importance of such ecosystems and the crucial role of satellite-data validation for reliable monitoring activities through remote sensing techniques drove the motivations and helped defining the main questions addressed in the present work

Performance of Ocean Colour Chlorophyll a algorithms for Sentinel-3 OLCI, MODIS-Aqua and Suomi-VIIRS in open-ocean waters of the Atlantic

This is the final version. Available on open access from Elsevier via the DOI in this recordThe proxy for phytoplankton biomass, Chlorophyll a (Chl a), is an important variable to assess the health and state of the oceans which are under increasing anthropogenic pressures. Prior to the operational use of satellite ocean-colour Chl a to monitor the oceans, rigorous assessments of algorithm performance are necessary to select the most suitable products. Due to their inaccessibility, the oligotrophic open-ocean gyres are under-sampled and therefore under-represented in global in situ data sets. The Atlantic Meridional Transect (AMT) campaigns fill the sampling gap in Atlantic oligotrophic waters. In-water underway spectrophotometric data were collected on three AMT field campaigns in 2016, 2017 and 2018 to assess the performance of Sentinel-3A (S3-A) and Sentinel-3B (S3-B) Ocean and Land Colour Instrument (OLCI) products. Three Chl a algorithms for OLCI were compared: Processing baseline (pb) 2, which uses the ocean colour 4 band ratio algorithm (OC4Me); pb 3 (OL_L2M.003.00) which uses OC4Me and a colour index (CI); and POLYMER v4.8 which models atmosphere and water reflectance and retrieves Chl a as a part of its spectral matching inversion. The POLYMER Chl a for S-3A OLCI performed best. The S-3A OLCI pb 2 tended to under-estimate Chl a especially at low concentrations, while the updated OL_L2M.003.00 provided significant improvements at low concentrations. OLCI data were also compared to MODIS-Aqua (R2018 processing) and Suomi-NPP VIIRS standard products. MODIS-Aqua exhibited good performance similar to OLCI POLYMER whereas Suomi-NPP VIIRS exhibited a slight under-estimate at higher Chl a values. The reasons for the differences were that S-3A OLCI pb 2 Rrs were over-estimated at blue bands which caused the under-estimate in Chl a. There were also some artefacts in the Rrs spectral shape of VIIRS which caused Chl a to be under-estimated at values >0.1 mg m-3. In addition, using in situ Rrs to compute Chl a with OC4Me we found a bias of 25% for these waters, related to the implementation of the OC4ME algorithm for S-3A OLCI. By comparison, the updated OLCI processor OL_L2M.003.00 significantly improved the Chl a retrievals at lower concentrations corresponding to the AMT measurements. S-3A and S-3B OLCI Chl a products were also compared during the Sentinel-3 mission tandem phase (the period when S-3A and S-3B were flying 30 sec apart along the same orbit). Both S-3A and S-3B OLCI pb 2 under-estimated Chl a especially at low values and the trend was greater for S-3A compared to S-3B. The performance of OLCI was improved by using either OL_L2M.003.00 or POLYMER Chl a. Analysis of coincident satellite images for S-3A OLCI, MODIS-Aqua and VIIRS as composites and over large areas illustrated that OLCI POLYMER gave the highest Chl a concentrations and percentage (%) coverage over the north and south Atlantic gyres, and OLCI pb 2 produced the lowest Chl a and % coverage.European Space Agency (ESA)Natural Environment Research Council (NERC)National Centre for Earth Observation (NCEO

Consistency between Satellite Ocean Colour Products under High Coloured Dissolved Organic Matter Absorption in the Baltic Sea

Ocean colour (OC) remote sensing is an important tool for monitoring phytoplankton in the global ocean. In optically complex waters such as the Baltic Sea, relatively efficient light absorption by substances other than phytoplankton increases product uncertainty. Sentinel-3 OLCI-A, Suomi-NPP VIIRS and MODIS-Aqua OC radiometric products were assessed using Baltic Sea in situ remote sensing reflectance

Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms

High resolution imaging spectrometers are prerequisite to address significant data gaps in inland optical water quality monitoring. In this work, we provide a data-driven alignment of chlorophyll-a and turbidity derived from the Sentinel-2 MultiSpectral Imager (MSI) with corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products. For chlorophyll-a retrieval, empirical ‘ocean colour’ blue-green band ratios and a near infra-red (NIR) band ratio algorithm, as well as a semi-analytical three-band NIR-red ratio algorithm, were included in the analysis. Six million co-registrations with MSI and OLCI spanning 24 lakes across five continents were analysed. Following atmospheric correction with POLYMER, the reflectance distributions of the red and NIR bands showed close similarity between the two sensors, whereas the distribution for blue and green bands was positively skewed in the MSI results compared to OLCI. Whilst it is not possible from this analysis to determine the accuracy of reflectance retrieved with either MSI or OLCI results, optimizing water quality algorithms for MSI against those previously derived for the Envisat Medium Resolution Imaging Spectrometer (MERIS) and its follow-on OLCI, supports the wider use of MSI for aquatic applications. Chlorophyll-a algorithms were thus tuned for MSI against concurrent OLCI observations, resulting in significant improvements against the original algorithm coefficients. The mean absolute difference (MAD) for the blue-green band ratio algorithm decreased from 1.95 mg m− 3 to 1.11 mg m− 3, whilst the correlation coefficient increased from 0.61 to 0.80. For the NIR-red band ratio algorithms improvements were modest, with the MAD decreasing from 4.68 to 4.64 mg m− 3 for the empirical red band ratio algorithm, and 3.73 to 3.67 for the semi-analytical 3-band algorithm. Three implementations of the turbidity algorithm showed improvement after tuning with the resulting distributions having reduced bias. The MAD reduced from 0.85 to 0.72, 1.22 to 1.10 and 1.93 to 1.55 FNU for the 665, 708 and 778 nm implementations respectively. However, several sources of uncertainty remain: adjacent land showed high divergence between the sensors, suggesting that high product uncertainty near land continues to be an issue for small water bodies, while it cannot be stated at this point whether MSI or OLCI results are differentially affected. The effect of spectrally wider bands of the MSI on algorithm sensitivity to chlorophyll-a and turbidity cannot be fully established without further availability of in situ optical measurements

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and interannual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided