Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29186
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dc.contributor.authorWarren, Mark Aen_UK
dc.contributor.authorSimis, Stefan G Hen_UK
dc.contributor.authorMartinez-Vicente, Victoren_UK
dc.contributor.authorPoser, Kathrinen_UK
dc.contributor.authorBresciani, Marianoen_UK
dc.contributor.authorAlikas, Kristaen_UK
dc.contributor.authorSpyrakos, Evangelosen_UK
dc.contributor.authorGiardino, Claudiaen_UK
dc.contributor.authorAnsper, Aveen_UK
dc.date.accessioned2019-04-03T00:03:18Z-
dc.date.available2019-04-03T00:03:18Z-
dc.date.issued2019-05en_UK
dc.identifier.urihttp://hdl.handle.net/1893/29186-
dc.description.abstractThe relatively high spatial resolution, short revisit time and red-edge spectral band (705 nm) of the ESA Sentinel-2 Multi Spectral Imager makes this sensor attractive for monitoring water quality of coastal and inland waters. Reliable atmospheric correction is essential to support routine retrieval of optically active substance concentration from water-leaving reflectance. In this study, six publicly available atmospheric correction algorithms (Acolite, C2RCC, iCOR, l2gen, Polymer and Sen2Cor) are evaluated against above-water optical in situ measurements, within a robust methodology, in two optically diverse coastal regions (Baltic Sea, Western Channel) and from 13 inland waterbodies from 5 European countries with a range of optical properties. The total number of match-ups identified for each algorithm ranged from 1059 to 1668 with 521 match-ups common to all algorithms. These in situ and MSI match-ups were used to generate statistics describing the performance of each algorithm for each respective region and a combined dataset. All ACs tested showed high uncertainties, in many cases >100% in the red and >1000% in the near-infra red bands. Polymer and C2RCC achieved the lowest root mean square differences (~0.0016 sr−1) and mean absolute differences (~40–60% in blue/green bands) across the different datasets. Retrieval of blue-green and NIR-red band ratios indicate that further work on AC algorithms is required to reproduce the spectral shape in the red and NIR bands needed to accurately retrieve the chlorophyll-a concentration in turbid waters.en_UK
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.relationWarren MA, Simis SGH, Martinez-Vicente V, Poser K, Bresciani M, Alikas K, Spyrakos E, Giardino C & Ansper A (2019) Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters. Remote Sensing of Environment, 225, pp. 267-289. https://doi.org/10.1016/j.rse.2019.03.018en_UK
dc.rightsThis article is available under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). You may copy and distribute the article, create extracts, abstracts and new works from the article, alter and revise the article, text or data mine the article and otherwise reuse the article commercially (including reuse and/or resale of the article) without permission from Elsevier. You must give appropriate credit to the original work, together with a link to the formal publication through the relevant DOI and a link to the Creative Commons user license above. You must indicate if any changes are made but not in any way that suggests the licensor endorses you or your use of the work.en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectAtmospheric correctionen_UK
dc.subjectSentinel 2en_UK
dc.subjectRemote sensing reflectanceen_UK
dc.subjectHyperspectral radiometryen_UK
dc.subjectBaltic Seaen_UK
dc.subjectLakesen_UK
dc.subjectWestern Channel Observatoryen_UK
dc.subjectCoastal watersen_UK
dc.subjectInland watersen_UK
dc.titleAssessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland watersen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1016/j.rse.2019.03.018en_UK
dc.citation.jtitleRemote Sensing of Environmenten_UK
dc.citation.issn0034-4257en_UK
dc.citation.volume225en_UK
dc.citation.spage267en_UK
dc.citation.epage289en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderEuropean Commissionen_UK
dc.citation.date22/03/2019en_UK
dc.contributor.affiliationPlymouth Marine Laboratoryen_UK
dc.contributor.affiliationPlymouth Marine Laboratoryen_UK
dc.contributor.affiliationPlymouth Marine Laboratoryen_UK
dc.contributor.affiliationWater Insight BVen_UK
dc.contributor.affiliationItalian National Research Council (CNR)en_UK
dc.contributor.affiliationUniversity of Tartuen_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationItalian National Research Council (CNR)en_UK
dc.contributor.affiliationUniversity of Tartuen_UK
dc.identifier.isiWOS:000469152700020en_UK
dc.identifier.scopusid2-s2.0-85063230396en_UK
dc.identifier.wtid1254295en_UK
dc.date.accepted2019-03-12en_UK
dcterms.dateAccepted2019-03-12en_UK
dc.date.filedepositdate2019-04-02en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorWarren, Mark A|en_UK
local.rioxx.authorSimis, Stefan G H|en_UK
local.rioxx.authorMartinez-Vicente, Victor|en_UK
local.rioxx.authorPoser, Kathrin|en_UK
local.rioxx.authorBresciani, Mariano|en_UK
local.rioxx.authorAlikas, Krista|en_UK
local.rioxx.authorSpyrakos, Evangelos|en_UK
local.rioxx.authorGiardino, Claudia|en_UK
local.rioxx.authorAnsper, Ave|en_UK
local.rioxx.projectProject ID unknown|European Commission (Horizon 2020)|en_UK
local.rioxx.freetoreaddate2019-04-02en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2019-04-02|en_UK
local.rioxx.filename1-s2.0-S0034425719301099-main.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source0034-4257en_UK
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