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An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

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National Centre for Earth Observation, Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
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Brockmann Consult, Max-Planck-Straße 2, D-21502 Geesthacht, Germany
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Marine Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
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PML Applications Ltd, Prospect Place, Plymouth PL1 3DH, UK
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Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
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Telespazio Vega UK for ESA Climate Office, European Space Agency/ECSAT, Harwell Campus OX11 0FD, UK
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Helmholtz-Zentrum Geesthacht, Zentrum für Material- und Küstenforschung GmbH, Max-Planck-Straße 1, D-21502 Geesthacht, Germany
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European Space Agency/ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands
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European Commission, Joint Research Centre (JRC), Via Enrico Fermi, 2749, I-21027 Ispra, Italy
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Telespazio VEGA UK Ltd., 350 Capability Green, Luton, Bedfordshire LU1 3LU, UK
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Solvo, 3 rue Saint-Antoine, 06600 Antibes, France
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Ocean Process Analysis Laboratory, Morse Hall, University of New Hampshire, Durham, NH 03824, USA
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European Space Agency, ESRIN, Via Galileo Galilei, Casella Postale 64, 00044 Frascati (Roma), Italy
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Department of Geography and Environmental Sciences, University of Reading, Whiteknights, Reading RG6 6DW, UK
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HYGEOS, 165 Avenue de Bretagne, 59000 Lille, France
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CNR-ISMAR, Via Fosso del Cavaliere, 100, 00133 Roma, Italy
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NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
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Scripps Institution of Oceanography Mail Code 0218, University of California San Diego, La Jolla, CA 92039-0218, USA
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Naval Research Laboratory, Bldg. 1009, Code 7331, Stennis Space Center, MS 39529, USA
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Department of Ecology, Environment and Plant Sciences, University of Stockholm, 106 91 Stockholm, Sweden
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Institute for Marine Remote Sensing, College of Marine Science, University of South Florida, 140 7th Ave. South St, Petersburg, FL 33701, USA
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Biology Department, MS 32, Woods Hole Oceanographic Institution, Woods Hole, MA 02543-1049, USA
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Department of Physics, University of Miami, James L. Knight Physics Building, 1320 Campo Sano Dr., Coral Gables, FL 33124, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4285; https://doi.org/10.3390/s19194285
Received: 27 July 2019 / Revised: 15 September 2019 / Accepted: 17 September 2019 / Published: 3 October 2019
(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)
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. View Full-Text
Keywords: ocean colour; water-leaving radiance; remote-sensing reflectance; phytoplankton; chlorophyll-a; inherent optical properties; Climate Change Initiative; optical water classes; Essential Climate Variable; uncertainty characterisation ocean colour; water-leaving radiance; remote-sensing reflectance; phytoplankton; chlorophyll-a; inherent optical properties; Climate Change Initiative; optical water classes; Essential Climate Variable; uncertainty characterisation
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Sathyendranath, S.; Brewin, R.J.; Brockmann, C.; Brotas, V.; Calton, B.; Chuprin, A.; Cipollini, P.; Couto, A.B.; Dingle, J.; Doerffer, R.; Donlon, C.; Dowell, M.; Farman, A.; Grant, M.; Groom, S.; Horseman, A.; Jackson, T.; Krasemann, H.; Lavender, S.; Martinez-Vicente, V.; Mazeran, C.; Mélin, F.; Moore, T.S.; Müller, D.; Regner, P.; Roy, S.; Steele, C.J.; Steinmetz, F.; Swinton, J.; Taberner, M.; Thompson, A.; Valente, A.; Zühlke, M.; Brando, V.E.; Feng, H.; Feldman, G.; Franz, B.A.; Frouin, R.; Gould, R.W.; Hooker, S.B.; Kahru, M.; Kratzer, S.; Mitchell, B.G.; Muller-Karger, F.E.; Sosik, H.M.; Voss, K.J.; Werdell, J.; Platt, T. An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors 2019, 19, 4285.

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