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dc.contributor.authorAsim, Muhammad
dc.contributor.authorBrekke, Camilla
dc.contributor.authorMahmood, Arif
dc.contributor.authorEltoft, Torbjørn
dc.contributor.authorReigstad, Marit
dc.date.accessioned2021-07-13T07:46:30Z
dc.date.available2021-07-13T07:46:30Z
dc.date.issued2021-04-22
dc.description.abstractThis article addresses methodologies for remote sensing of ocean Chlorophyll-a (Chl-a), with emphasis on the Barents Sea. We aim at improving the monitoring capacity by integrating in situ Chl-a observations and optical remote sensing to locally train machine learning (ML) models. For this purpose, in situ measurements of Chl-a ranging from 0.014–10.81 mg/m <sup>3</sup> , collected for the years 2016–2018, were used to train and validate models. To accurately estimate Chl-a, we propose to use additional information on pigment content within the productive column by matching the depth-integrated Chl-a concentrations with the satellite data. Using the optical images captured by the multispectral imager instrument on Sentinel-2 and the in situ measurements, a new spatial window-based match-up dataset creation method is proposed to increase the number of match-ups and hence improve the training of the ML models. The match-ups are then filtered to eliminate erroneous samples based on the spectral distribution of the remotely sensed reflectance. In addition, we design and implement a neural network model dubbed as the ocean color net (OCN), that has performed better than existing ML-based techniques, including the Gaussian process Regression (GPR), regionally tuned empirical techniques, including the ocean color (OC3) algorithm and the spectral band ratios, as well as the globally trained Case-2 regional/coast colour (C2RCC) processing chain model C2RCC-networks. The proposed OCN model achieved reduced mean absolute error compared to the GPR by 5.2%, C2RCC by 51.7%, OC3 by 22.6%, and spectral band ratios by 29%. Moreover, the proposed spatial window and depth-integrated match-up creation techniques improved the performance of the proposed OCN by 57%, GPR by 41.9%, OC3 by 5.3%, and spectral band ratio method by 24% in terms of RMSE compared to the conventional match-up selection approach.en_US
dc.identifier.citationAsim M, Brekke C, Mahmood A, Eltoft T, Reigstad M. Improving Chlorophyll-a Estimation from Sentinel-2 (MSI) in the Barents Sea using Machine Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021en_US
dc.identifier.cristinIDFRIDAID 1903612
dc.identifier.doihttps://doi.org/10.1109/JSTARS.2021.3074975
dc.identifier.issn1939-1404
dc.identifier.issn2151-1535
dc.identifier.urihttps://hdl.handle.net/10037/21863
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofAsim, M. (2023). Optical remote sensing of water quality parameters retrieval in the Barents Sea. (Doctoral thesis). <a href=https://hdl.handle.net/10037/28787>https://hdl.handle.net/10037/28787</a>.
dc.relation.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.relation.projectIDNorges forskningsråd: 27673en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/NANSEN/276730/Norway/The Nansen Legacy/NANSEN/en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.titleImproving Chlorophyll-a Estimation from Sentinel-2 (MSI) in the Barents Sea using Machine Learningen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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