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dc.contributor.authorLosa, Svetlana N.
dc.contributor.authorSoppa, Mariana A.
dc.contributor.authorDinter, Tilman
dc.contributor.authorWolanin, Aleksandra
dc.contributor.authorBrewin, Robert J. W.
dc.contributor.authorBricaud, Annick
dc.contributor.authorOelker, Julia
dc.contributor.authorPeeken, Ilka
dc.contributor.authorGentili, Bernard
dc.contributor.authorRozanov, Vladimir
dc.contributor.authorBracher, Astrid
dc.identifier.citationLosa, S. N., Soppa, M. A., Dinter, T., Wolanin, A., Brewin, R. J. W., et al. (2017) Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT). Frontiers in Marine Science, 4:00203, 22pp. DOI:
dc.description.abstractWe derive the chlorophyll a concentration (Chla) for three main phytoplankton functional types (PFTs) – diatoms, coccolithophores and cyanobacteria – by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral satellite measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm applied to the Ocean Color Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 March 2012 and evaluated against PFT Chla data obtained from in situ marker pigment data and the NASA Ocean Biogeochemical Model simulations and satellite information on phytoplankton size. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted.en_US
dc.rightsAttribution 4.0 International*
dc.subject.otherMultispectral-based PFT Chla retrievalsen_US
dc.subject.otherChlorophyll a concentrationen_US
dc.subject.otherPhytoplankton functional types (PFTs)en_US
dc.titleSynergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT).en_US
dc.typeJournal Contributionen_US
dc.subject.dmProcessesData analysisen_US
dc.subject.dmProcessesData acquisitionen_US
dc.subject.dmProcessesData aggregationen_US
dc.bibliographicCitation.titleFrontiers in Marine Scienceen_US
dc.bibliographicCitation.issueArticle 00203en_US
dc.description.maturitylevelPilot or Demonstrateden_US
dc.description.adoptionNovel (no adoption outside originators)en_US
dc.description.sensorsMultispectral ocean color sensorsen_US
dc.description.sensorsSCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY)en_US
dc.description.methodologyTypeMethoden_US N. Losa

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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International