| Registro completo |
| Provedor de dados: |
ArchiMer
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| País: |
France
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| Título: |
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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| Autores: |
Sathyendranath, Shubha
Brewin, Robert
Brockmann, Carsten
Brotas, Vanda
Calton, Ben
Chuprin, Andrei
Cipollini, Paolo
Couto, André
Dingle, James
Doerffer, Roland
Donlon, Craig
Dowell, Mark
Farman, Alex
Grant, Mike
Groom, Steve
Horseman, Andrew
Jackson, Thomas
Krasemann, Hajo
Lavender, Samantha
Martinez-vicente, Victor
Mazeran, Constant
Mélin, Frédéric
Moore, Timothy
Müller, Dagmar
Regner, Peter
Roy, Shovonlal
Steele, Chris
Steinmetz, François
Swinton, John
Taberner, Malcolm
Thompson, Adam
Valente, André
Zühlke, Marco
Brando, Vittorio
Feng, Hui
Feldman, Gene
Franz, Bryan
Frouin, Robert
Gould, Richard
Hooker, Stanford
Kahru, Mati
Kratzer, Susanne
Mitchell, B.
Muller-karger, Frank
Sosik, Heidi
Voss, Kenneth
Werdell, Jeremy
Platt, Trevor
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| Data: |
2019-10
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| Ano: |
2019
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| Palavras-chave: |
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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| Resumo: |
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.
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| Tipo: |
Text
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| Idioma: |
Inglês
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| Identificador: |
https://archimer.ifremer.fr/doc/00589/70072/68045.pdf
DOI:10.3390/s19194285
https://archimer.ifremer.fr/doc/00589/70072/
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| Editor: |
MDPI AG
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| Formato: |
application/pdf
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| Fonte: |
Sensors (1424-8220) (MDPI AG), 2019-10 , Vol. 19 , N. 19 , P. 4285 (31p.)
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| Direitos: |
info:eu-repo/semantics/openAccess
restricted use
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