Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/99360
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.contributorResearch Institute for Land and Spaceen_US
dc.creatorHafeez, Sen_US
dc.creatorWong, MSen_US
dc.creatorAbbas, Sen_US
dc.creatorAsim, Men_US
dc.date.accessioned2023-07-07T08:28:44Z-
dc.date.available2023-07-07T08:28:44Z-
dc.identifier.urihttp://hdl.handle.net/10397/99360-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rightsCopyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en_US
dc.rightsThe following publication Hafeez, S.;Wong, M.S.; Abbas, S.; Asim, M. Evaluating Landsat-8 and Sentinel-2 Data Consistency for High Spatiotemporal Inland and CoastalWater Quality Monitoring. Remote Sens. 2022, 14, 3155 is available at https://doi.org/10.3390/rs14133155.en_US
dc.subjectFloating algae bloomen_US
dc.subjectLandsaten_US
dc.subjectSentinelen_US
dc.subjectSpectral adjustmenten_US
dc.subjectTime seriesen_US
dc.subjectTSS concentrationen_US
dc.subjectWater qualityen_US
dc.subjectWater-leaving reflectanceen_US
dc.titleEvaluating landsat-8 and sentinel-2 data consistency for high spatiotemporal inland and coastal water quality monitoringen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume14en_US
dc.identifier.issue13en_US
dc.identifier.doi10.3390/rs14133155en_US
dcterms.abstractThe synergy of fine-to-moderate-resolutin (i.e., 10–60 m) satellite data of the Landsat-8 Operational Land Imager (OLI) and the Sentinel-2 Multispectral Instrument (MSI) provides a possibility to monitor the dynamics of sensitive aquatic systems. However, it is imperative to assess the spectral consistency of both sensors before developing new algorithms for their combined use. This study evaluates spectral consistency between OLI and MSI-A/B, mainly in terms of the top-of-atmosphere reflectance (t), Rayleigh-corrected reflectance (rc), and remote-sensing reflectance (Rrs). To check the spectral consistency under various atmospheric and aquatic conditions, nearsimultaneous same-day overpass images of OLI and MSI-A/B were selected over diverse coastal and inland areas across Mainland China and Hong Kong. The results showed that spectral data obtained from OLI and MSI-A/B were consistent. The difference in the mean absolute percentage error (MAPE) of the OLI and MSI-A products was ~8% in t and ~10% in both rc and Rrs for all the matching bands, whereas the MAPE for OLI and MSI-B was ~3.7% in t, ~5.7% in rc, and ~7.5% in Rrs for all visible bands except the ultra-blue band. Overall, the green band was the most consistent, with the lowest MAPE of ≤4.6% in all the products. The linear regression model suggested that product difference decreased significantly after band adjustment with the highest reduction rate in Rrs (NIR band) and Rrs (red band) for the OLI–MSI-A and OLI–MSI-B comparison, respectively. Further, this study discussed the combined use of OLI and MSI-A/B data for (i) time series of the total suspended solid concentrations (TSS) over coastal and inland waters; (ii) floating algae area comparison; and (iii) tracking changes in coastal floating algae (FA). Time series analysis of the TSS showed that seasonal variation was well-captured by the combined use of sensors. The analysis of the floating algae bloom area revealed that the algae area was consistent, however, the difference increases as the time difference between the same-day overpasses increases. Furthermore, tracking changes in coastal FA over two months showed that thin algal slicks (width < 500 m) can be detected with an adequate spatial resolution of the OLI and the MSI.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationRemote sensing, July 2022, v. 14, no. 13, 3155en_US
dcterms.isPartOfRemote sensingen_US
dcterms.issued2022-07-
dc.identifier.scopus2-s2.0-85133693851-
dc.identifier.eissn2072-4292en_US
dc.identifier.artn3155en_US
dc.description.validate202307 bcwwen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera2219-
dc.identifier.SubFormID47083-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextthe Research Institute for Land and Space (project ID 1-CD81), The Hong Kong Polytechnic University.en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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