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dc.contributor.authorMouw, CB
dc.contributor.authorHardman-Mountford, NJ
dc.contributor.authorAlvain, S
dc.contributor.authorBracher, A
dc.contributor.authorBrewin, RJW
dc.contributor.authorBricaud, A
dc.contributor.authorCiotti, AM
dc.contributor.authorDevred, E
dc.contributor.authorFujiwara, A
dc.contributor.authorHirata, T
dc.contributor.authorHirawake, T
dc.contributor.authorKostadinov, TS
dc.contributor.authorRoy, S
dc.contributor.authorUitz, J
dc.date.accessioned2019-08-07T10:02:41Z
dc.date.issued2017-02-21
dc.description.abstractPhytoplankton are composed of diverse taxonomical groups, which are manifested as distinct morphology, size, and pigment composition. These characteristics, modulated by their physiological state, impact their light absorption and scattering, allowing them to be detected with ocean color satellite radiometry. There is a growing volume of literature describing satellite algorithms to retrieve information on phytoplankton composition in the ocean. This synthesis provides a review of current methods and a simplified comparison of approaches. The aim is to provide an easily comprehensible resource for non-algorithm developers, who desire to use these products, thereby raising the level of awareness and use of these products and reducing the boundary of expert knowledge needed to make a pragmatic selection of output products with confidence. The satellite input and output products, their associated validation metrics, as well as assumptions, strengths, and limitations of the various algorithm types are described, providing a framework for algorithm organization to assist users and inspire new aspects of algorithm development capable of exploiting the higher spectral, spatial and temporal resolutions from the next generation of ocean color satellites.en_GB
dc.description.sponsorshipThe National Aeronautics and Space Administration (NASA)en_GB
dc.description.sponsorshipESA SEOM SY-4Sci Synergy project SynSenPFTen_GB
dc.identifier.citationVol. 4en_GB
dc.identifier.doi10.3389/fmars.2017.00041
dc.identifier.grantnumberNNX13AC34Gen_GB
dc.identifier.grantnumberNNX13AC92Gen_GB
dc.identifier.urihttp://hdl.handle.net/10871/38257
dc.language.isoenen_GB
dc.publisherFrontiers Mediaen_GB
dc.rights© 2017 Mouw, Hardman-Mountford, Alvain, Bracher, Brewin, Bricaud, Ciotti, Devred, Fujiwara, Hirata, Hirawake, Kostadinov, Roy and Uitz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_GB
dc.subjectremote sensingen_GB
dc.subjectocean coloren_GB
dc.subjectopticsen_GB
dc.subjectphytoplankton functional typesen_GB
dc.subjectphytoplankton size classesen_GB
dc.subjectparticle size distributionen_GB
dc.subjectphytoplankton taxonomic compositionen_GB
dc.subjectbio-optical algorithmsen_GB
dc.titleA consumer's guide to satellite remote sensing of multiple phytoplankton groups in the global oceanen_GB
dc.typeArticleen_GB
dc.date.available2019-08-07T10:02:41Z
dc.descriptionThis is the final version. Available from Frontiers Media via the DOI in this recorden_GB
dc.identifier.journalFrontiers in Marine Scienceen_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2017-02-03
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2017-02-21
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2019-08-07T09:57:09Z
refterms.versionFCDVoR
refterms.dateFOA2019-08-07T10:02:44Z
refterms.panelCen_GB


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