Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/33255
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dc.contributor.authorGonzález Vilas, Luisen_UK
dc.contributor.authorSpyrakos, Evangelosen_UK
dc.contributor.authorTorres Palenzuela, Jesus Men_UK
dc.date.accessioned2021-09-09T00:03:32Z-
dc.date.available2021-09-09T00:03:32Z-
dc.date.issued2011-02-15en_UK
dc.identifier.urihttp://hdl.handle.net/1893/33255-
dc.description.abstractIn typical Case 2 waters, accurate remote sensing retrieval of chlorophyll a (chla) is still a challenging task. In this study, focusing on the Galician rias (ΝW Spain), algorithms based on neural network (NN) techniques were developed for the retrieval of chla concentration in optically complex waters, using Medium Resolution Imaging Spectrometer (MERIS) data. There is considerable interest in the accurate estimation of chla for the Galician rias, because of the economic and social importance of the extensive culture of mussels, and the high frequency of harmful algal events. Fifteen MERIS full resolution (FR) cloud-free images paired with in situ chla data (for 2002–2004 and 2006–2008) were used for the development and validation of the NN. The scope of NN was established from the clusters obtained using fuzzy c-mean (FCM) clustering techniques applied to the satellite-derived data. Three different NNs were developed: one including the whole data set, and two others using only points belonging to one of the clusters. The input data for these latter two NNs was chosen depending on the quality level, defined on the basis of quality flags given to each data set. The fitting results were fairly good and proved the capability of the tool to predict chla concentrations in the study area. The best prediction was given for the NN trained with high-quality data using the most abundant cluster data set. The performance parameters in the validation set of this NN were R2 = 0.86, mean percentage error (MPE) = − 0.14, root mean square error (RMSE) = 0.75 mg m− 3, and relative RMSE = 66%. The NN developed in this study detected accurately the peaks of chla, in both training and validation sets. The performance of the Case-2-Regional (C2R) algorithm, routinely used for MERIS data, was also tested and compared with our best performing NN and the sea-truthing data. Results showed that this NN outperformed the C2R, giving much higher R2 and lower RMSE values. This study showed that the combination of in situ data and NN technology improved the retrieval of chla in Case 2 waters, and could be used to obtain more accurate chla maps. A local-based algorithm for the chla retrieval from an ocean colour sensor with the characteristics of MERIS would be a great support in the quantitative monitoring and study of harmful algal events in the coastal waters of the Rias Baixas. The limitations and possible improvements of the developed chla algorithms are also discussed.en_UK
dc.language.isoenen_UK
dc.publisherElsevier BVen_UK
dc.relationGonzález Vilas L, Spyrakos E & Torres Palenzuela JM (2011) Neural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain). Remote Sensing of Environment, 115 (2), pp. 524-535. https://doi.org/10.1016/j.rse.2010.09.021en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.rights.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserveden_UK
dc.subjectChlorophyll aen_UK
dc.subjectGalician riasen_UK
dc.subjectNeural networken_UK
dc.subjectMERISen_UK
dc.subjectCase 2 watersen_UK
dc.subjectAlgorithm developmenten_UK
dc.titleNeural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain)en_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2999-12-31en_UK
dc.rights.embargoreason[1-s2.0-S0034425710002944-main.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1016/j.rse.2010.09.021en_UK
dc.citation.jtitleRemote Sensing of Environmenten_UK
dc.citation.issn0034-4257en_UK
dc.citation.volume115en_UK
dc.citation.issue2en_UK
dc.citation.spage524en_UK
dc.citation.epage535en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderEuropean Commission (Horizon 2020)en_UK
dc.author.emailevangelos.spyrakos@stir.ac.uken_UK
dc.citation.date23/10/2010en_UK
dc.contributor.affiliationUniversity of Vigoen_UK
dc.contributor.affiliationUniversity of Vigoen_UK
dc.contributor.affiliationUniversity of Vigoen_UK
dc.identifier.isiWOS:000286782500023en_UK
dc.identifier.scopusid2-s2.0-78650923209en_UK
dc.identifier.wtid1450590en_UK
dc.date.accepted2010-11-27en_UK
dcterms.dateAccepted2010-11-27en_UK
dc.date.filedepositdate2021-09-08en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorGonzález Vilas, Luis|en_UK
local.rioxx.authorSpyrakos, Evangelos|en_UK
local.rioxx.authorTorres Palenzuela, Jesus M|en_UK
local.rioxx.projectProject ID unknown|European Commission (Horizon 2020)|en_UK
local.rioxx.freetoreaddate2260-09-24en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||en_UK
local.rioxx.filename1-s2.0-S0034425710002944-main.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source0034-4257en_UK
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