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dc.contributor.authorLi, Ruibo
dc.contributor.authorLi, Hua
dc.contributor.authorSun, Lin
dc.contributor.authorYang, Yikun
dc.contributor.authorHu, Tian
dc.contributor.authorBian, Zunjian
dc.contributor.authorCao, Biao
dc.contributor.authorDu, Yongming
dc.contributor.authorLiu, Qinhuo
dc.date.accessioned2020-09-23T01:19:13Z
dc.date.available2020-09-23T01:19:13Z
dc.date.issued2020
dc.identifier.issn2072-4292en_US
dc.identifier.doi10.3390/rs12162613en_US
dc.identifier.urihttp://hdl.handle.net/10072/397825
dc.description.abstractAn operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.language.isoeng
dc.publisherMDPIen_US
dc.relation.ispartofpagefrom2613en_US
dc.relation.ispartofissue16en_US
dc.relation.ispartofjournalRemote Sensingen_US
dc.relation.ispartofvolume12en_US
dc.subject.fieldofresearchClassical Physicsen_US
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscienceen_US
dc.subject.fieldofresearchGeomatic Engineeringen_US
dc.subject.fieldofresearchcode0203en_US
dc.subject.fieldofresearchcode0406en_US
dc.subject.fieldofresearchcode0909en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsRemote Sensingen_US
dc.subject.keywordsHimawari-8 AHIen_US
dc.subject.keywordsoperational split-window algorithmen_US
dc.titleAn Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Dataen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationLi, R; Li, H; Sun, L; Yang, Y; Hu, T; Bian, Z; Cao, B; Du, Y; Liu, Q, An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data, Remote Sensing, 2020, 12 (16), pp. 2613en_US
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/en_US
dc.date.updated2020-09-23T00:45:37Z
dc.description.versionVersion of Record (VoR)en_US
gro.rights.copyright© 2020 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 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
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gro.griffith.authorHu, Tian


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