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dc.contributor.authorBrewin, Robert J.W.
dc.contributor.authorRaitsos, Dionysios E.
dc.contributor.authorDall'Olmo, Giorgio
dc.contributor.authorZarokanellos, Nikolaos
dc.contributor.authorJackson, Thomas
dc.contributor.authorRacault, Marie-Fanny
dc.contributor.authorBoss, Emmanuel S.
dc.contributor.authorSathyendranath, Shubha
dc.contributor.authorJones, Burton
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2015-08-18T08:25:59Z
dc.date.available2015-08-18T08:25:59Z
dc.date.issued2015-05-19
dc.identifier.citationRegional ocean-colour chlorophyll algorithms for the Red Sea 2015, 165:64 Remote Sensing of Environment
dc.identifier.issn00344257
dc.identifier.doi10.1016/j.rse.2015.04.024
dc.identifier.urihttp://hdl.handle.net/10754/574977
dc.description.abstractThe Red Sea is a semi-enclosed tropical marine ecosystem that stretches from the Gulf of Suez and Gulf of Aqaba in the north, to the Gulf of Aden in the south. Despite its ecological and economic importance, its biological environment is relatively unexplored. Satellite ocean-colour estimates of chlorophyll concentration (an index of phytoplankton biomass) offer an observational platform to monitor the health of the Red Sea. However, little is known about the optical properties of the region. In this paper, we investigate the optical properties of the Red Sea in the context of satellite ocean-colour estimates of chlorophyll concentration. Making use of a new merged ocean-colour product, from the European Space Agency (ESA) Climate Change Initiative, and in situ data in the region, we test the performance of a series of ocean-colour chlorophyll algorithms. We find that standard algorithms systematically overestimate chlorophyll when compared with the in situ data. To investigate this bias we develop an ocean-colour model for the Red Sea, parameterised to data collected during the Tara Oceans expedition, that estimates remote-sensing reflectance as a function of chlorophyll concentration. We used the Red Sea model to tune the standard chlorophyll algorithms and the overestimation in chlorophyll originally observed was corrected. Results suggest that the overestimation was likely due to an excess of CDOM absorption per unit chlorophyll in the Red Sea when compared with average global conditions. However, we recognise that additional information is required to test the influence of other potential sources of the overestimation, such as aeolian dust, and we discuss uncertainties in the datasets used. We present a series of regional chlorophyll algorithms for the Red Sea, designed for a suite of ocean-colour sensors, that may be used for further testing.
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0034425715001662
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 18 May 2015. DOI: 10.1016/j.rse.2015.04.024
dc.subjectPhytoplankton
dc.subjectOcean colour
dc.subjectRemote sensing
dc.subjectChlorophyll
dc.subjectRed Sea
dc.subjectValidation
dc.subjectColoured dissolved organic matter
dc.titleRegional ocean-colour chlorophyll algorithms for the Red Sea
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentMarine Science Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.identifier.journalRemote Sensing of Environment
dc.eprint.versionPost-print
dc.contributor.institutionPlymouth Marine Laboratory (PML), Prospect Place, The Hoe, Plymouth PL1 3DH, UK
dc.contributor.institutionNational Centre for Earth Observation, PML, Plymouth PL1 3DH, UK
dc.contributor.institutionSchool of Marine Sciences, University of Maine, Orono, ME 04469-5741, USA
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personZarokanellos, Nikolaos
kaust.personJones, Burton
kaust.personHoteit, Ibrahim
refterms.dateFOA2017-05-18T00:00:00Z
dc.date.published-online2015-05-19
dc.date.published-print2015-08


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