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Cited 71 time in webofscience Cited 81 time in scopus
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Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa

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dc.contributor.authorBuma, Willibroad Gabila-
dc.contributor.authorLee, Sang-Il-
dc.date.accessioned2023-04-27T22:40:41Z-
dc.date.available2023-04-27T22:40:41Z-
dc.date.issued2020-08-
dc.identifier.issn2072-4292-
dc.identifier.issn2072-4292-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/6394-
dc.description.abstractMuch effort has been applied in estimating the concentrations of chlorophyll-a (Chla) in lakes. The optical complexity and lack of in situ data complicate estimating Chlain such water bodies.We compared four established satellite reflectance algorithms-the two-band and three-band algorithms (2BDA, 3BDA), fluorescence line height (FLH), and normalized difference chlorophyll index (NDCI)-to estimate Chlaconcentration in Lake Chad. We evaluated the performance and applicability of Landsat-8 (L8) and Sentinel-2 (S2) images with the four Chlaestimation algorithms. For accuracy, we compared the concentration levels from the four algorithms to those from Worldview-3 (WV3) images. We identified two promising algorithms that could be used alongside L8 and S2 satellite images to monitor Chlaconcentrations in Lake Chad. With an averaged R(2)of 0.8, the 3BDA and NDCI Chlaalgorithms performed accurately with S2 and L8 images. For the S2 and L8 images, 3BDA had the highest performance when compared to the WV3 estimates. We demonstrate the usefulness of sensor images in improving water quality information for areas that are difficult to access or when conventional data are limited.-
dc.language영어-
dc.language.isoENG-
dc.publisherMDPI-
dc.titleEvaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa-
dc.typeArticle-
dc.publisher.location스위스-
dc.identifier.doi10.3390/rs12152437-
dc.identifier.scopusid2-s2.0-85089548163-
dc.identifier.wosid000567213000001-
dc.identifier.bibliographicCitationREMOTE SENSING, v.12, no.15-
dc.citation.titleREMOTE SENSING-
dc.citation.volume12-
dc.citation.number15-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEnvironmental Sciences & Ecology-
dc.relation.journalResearchAreaGeology-
dc.relation.journalResearchAreaRemote Sensing-
dc.relation.journalResearchAreaImaging Science & Photographic Technology-
dc.relation.journalWebOfScienceCategoryEnvironmental Sciences-
dc.relation.journalWebOfScienceCategoryGeosciences, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryRemote Sensing-
dc.relation.journalWebOfScienceCategoryImaging Science & Photographic Technology-
dc.subject.keywordPlusREFLECTANCE ALGORITHMS-
dc.subject.keywordPlusPHYTOPLANKTON BLOOMS-
dc.subject.keywordPlusREMOTE ESTIMATION-
dc.subject.keywordPlusWATER-
dc.subject.keywordPlusINDEX-
dc.subject.keywordPlusVALIDATION-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusVARIABILITY-
dc.subject.keywordPlusVEGETATION-
dc.subject.keywordPlusPATTERNS-
dc.subject.keywordAuthorLake Chad-
dc.subject.keywordAuthorLandsat-
dc.subject.keywordAuthorSentinel-
dc.subject.keywordAuthorWorldView-
dc.subject.keywordAuthorChlorophyll-a-
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