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dc.contributor.authorAsim, Muhammad
dc.contributor.authorMatsuoka, Atsushi
dc.contributor.authorEllingsen, Pål Gunnar
dc.contributor.authorBrekke, Camilla
dc.contributor.authorEltoft, Torbjørn
dc.contributor.authorBlix, Katalin
dc.date.accessioned2023-02-20T13:33:43Z
dc.date.available2023-02-20T13:33:43Z
dc.date.issued2022-12-12
dc.description.abstractThe synergistic use of Landsat-8 operational land imager (OLI) and Sentinel-2 multispectral instrument (MSI) data products provides an excellent opportunity to monitor the dynamics of aquatic ecosystems. However, the merging of data products from multisensors is often adversely affected by the difference in their spectral characteristics. In addition, the errors in the atmospheric correction (AC) methods further increase the inconsistencies in downstream products. This work proposes an improved spectral harmonization method for OLI and MSI-derived remote sensing reflectance ( <i>R<sub>rs</sub></i> ) products, which significantly reduces uncertainties compared to those in the literature. We compared <i>R<sub>rs</sub></i> retrieved via state-of-the-art AC processors, i.e., Acolite, C2RCC, and Polymer, against ship-based in situ <i>R<sub>rs</sub></i> observations obtained from the Barents Sea waters, including a wide range of optical properties. Results suggest that the Acolite-derived <i>R<sub>rs</sub></i> has a minimum bias for our study area with median absolute percentage difference (MAPD) varying from 9% to 25% in the blue–green bands. To spectrally merge OLI and MSI, we develop and apply a new machine learning-based bandpass adjustment (BA) model to near-simultaneous OLI and MSI images acquired in the years from 2018 to 2020. Compared to a conventional linear adjustment, we demonstrate that the spectral difference is significantly reduced from ∼6 % to 12% to ∼2 % to <10% in the common OLI-MSI bands using the proposed BA model. The findings of this study are useful for the combined use of OLI and MSI <i>R<sub>rs</sub></i> products for water quality monitoring applications. The proposed method has the potential to be applied to other waters.en_US
dc.identifier.citationAsim M, Matsuoka A, Ellingsen PG, Brekke C, Eltoft T, Blix K. A new spectral harmonization algorithm for Landsat-8 and Sentinel-2 remote sensing reflectance products using machine learning: a case study for the Barents Sea (European Arctic). IEEE Transactions on Geoscience and Remote Sensing. 2022en_US
dc.identifier.cristinIDFRIDAID 2101740
dc.identifier.doi10.1109/TGRS.2022.3228393
dc.identifier.issn0196-2892
dc.identifier.issn1558-0644
dc.identifier.urihttps://hdl.handle.net/10037/28578
dc.language.isoengen_US
dc.publisherIEEEen_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 Transactions on Geoscience and Remote Sensing
dc.relation.projectIDNorges forskningsråd: 276730en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleA new spectral harmonization algorithm for Landsat-8 and Sentinel-2 remote sensing reflectance products using machine learning: a case study for the Barents Sea (European Arctic)en_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)