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dc.contributor.authorGittings, JA
dc.contributor.authorBrewin, RJW
dc.contributor.authorRaitsos, DE
dc.contributor.authorKheireddine, M
dc.contributor.authorOuhssain, M
dc.contributor.authorJones, BH
dc.contributor.authorHoteit, I
dc.date.accessioned2019-12-12T15:29:52Z
dc.date.issued2019-10-09
dc.description.abstractPhytoplankton size structure impacts ocean food-web dynamics and biogeochemical cycling, and is thus an important ecological indicator that can be utilised to quantitatively evaluate the state of marine ecosystems. Potential alterations to size structure are predicted to occur in tropical regions under future scenarios of climate change. Therefore, there is an increasing requirement for the synoptic monitoring of phytoplankton size structure in marine systems. The Red Sea remains a comparatively unexplored tropical marine ecosystem, particularly with regards to its large-scale biological dynamics. Using an in situ pigment dataset acquired in the Red Sea, we parameterise a two-component, abundance-based phytoplankton size model and apply it to remotely-sensed observations of chlorophyll-a (Chl-a) concentration, to infer Chl-a in two size classes of phytoplankton, small cells <2 μm in size (picophytoplankton) and large cells >2 μm in size. Satellite-derived estimates of phytoplankton size structure are in good agreement with corresponding in situ measurements and also capture the spatial variability related to regional mesoscale dynamics. Our analysis reveals that, for the estimation of Chl-a in the two size classes, the model performs comparably or in some cases better, to validations in other oceanic regions. Our model parameterisation will be useful for future studies on the seasonal and interannual variability of phytoplankton size classes in the Red Sea, which may ultimately be relevant for understanding trophic linkages between phytoplankton size structure and fisheries, and the development of marine management strategies.en_GB
dc.description.sponsorshipKAUST Office of Sponsored Research (OSR)en_GB
dc.description.sponsorshipVirtual Red Sea Initiativeen_GB
dc.identifier.citationVol. 234, article 111387en_GB
dc.identifier.doi10.1016/j.rse.2019.111387
dc.identifier.grantnumberURF/1/2979-01-01en_GB
dc.identifier.grantnumberREP/1/3268-01-01en_GB
dc.identifier.grantnumberBAS/1/1032-01-01en_GB
dc.identifier.urihttp://hdl.handle.net/10871/40087
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.rights.embargoreasonUnder embargo until 9 October 2020 in compliance with publisher policyen_GB
dc.rights© 2019. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/  en_GB
dc.subjectOcean colouren_GB
dc.subjectRemote sensingen_GB
dc.subjectPhytoplanktonen_GB
dc.subjectSize structureen_GB
dc.subjectChlorophyllen_GB
dc.subjectRed Seaen_GB
dc.titleRemotely sensing phytoplankton size structure in the Red Seaen_GB
dc.typeArticleen_GB
dc.date.available2019-12-12T15:29:52Z
dc.identifier.issn0034-4257
dc.descriptionThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recorden_GB
dc.identifier.journalRemote Sensing of Environmenten_GB
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/  en_GB
dcterms.dateAccepted2019-08-22
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2019-10-09
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2019-12-12T15:26:25Z
refterms.versionFCDAM
refterms.panelCen_GB


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© 2019. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/  
Except where otherwise noted, this item's licence is described as © 2019. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/