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dc.contributor.authorBlix, Katalin
dc.contributor.authorPálffy, Károly
dc.contributor.authorTóth, Viktor R.
dc.contributor.authorEltoft, Torbjørn
dc.date.accessioned2018-10-25T13:49:56Z
dc.date.available2018-10-25T13:49:56Z
dc.date.issued2018-10-11
dc.description.abstractThe Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology.en_US
dc.description.sponsorshipCIRFA partnersen_US
dc.descriptionSource at <a href=https://doi.org/10.3390/w10101428> https://doi.org/10.3390/w10101428</a>.en_US
dc.identifier.citationBlix, K., Pálffy, K., Tóth, V.R. & Eltoft, T. (2018). Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI. Water, 10(10). https://doi.org/10.3390/w10101428en_US
dc.identifier.cristinIDFRIDAID 1620136
dc.identifier.doi10.3390/w10101428
dc.identifier.issn2073-4441
dc.identifier.urihttps://hdl.handle.net/10037/14037
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofBlix, K. (2019). Machine Learning Water Quality Monitoring. (Doctoral thesis). <a href=https://hdl.handle.net/10037/16502>https://hdl.handle.net/10037/16502</a>.
dc.relation.journalWater
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.subjectVDP::Mathematics and natural science: 400::Zoology and botany: 480::Limnology: 498en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Limnologi: 498en_US
dc.subjectshallow lakeen_US
dc.subjectChl-aen_US
dc.subjectCDOMen_US
dc.subjectTSMen_US
dc.subjectGaussian process regressionen_US
dc.subjectautomatic model selection algorithmen_US
dc.titleRemote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCIen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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