Determination of land surface temperature using precipitable water based Split-Window and Artificial Neural Network in Turkey

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dc.authoridsahin, mehmet/0000-0001-7942-9253
dc.contributor.authorYildiz, B. Yigit
dc.contributor.authorSahin, Mehmet
dc.contributor.authorSenkal, Ozan
dc.contributor.authorPestimalci, Vedat
dc.contributor.authorTepecik, Kadir
dc.date.accessioned2024-12-24T19:25:21Z
dc.date.available2024-12-24T19:25:21Z
dc.date.issued2014
dc.departmentSiirt Üniversitesi
dc.description.abstractLand surface temperature (LST) calculation utilizing satellite thermal images is very difficult due to the great temporal variance of atmospheric water vapor in the atmosphere which strongly affects the thermal radiance incoming to satellite sensors. In this study, Split-Window (SW) and Radial Basis Function (RBF) methods were utilized for prediction of LST using precipitable water for Turkey. Coll 94 Split-Window algorithm was modified using regional precipitable water values estimated from upper-air Radiosond observations for the years 1990-2007 and Local Split-Window (LSW) algorithms were generated for the study area. Using local algorithms and Advanced Very High Resolution Radiometer (AVHRR) data, monthly mean daily sum LST values were calculated. In RBF method latitude, longitude, altitude, surface emissivity, sun shine duration and precipitable water values were used as input variables of the structure. Correlation coefficients between estimated and measured LST values were obtained as 99.23% (for RBF) and 94.48% (for LSW) at 00:00 UTC and 92.77% (for RBF) and 89.98% (for LSW) at 12:00 UTC. These meaningful statistical results suggest that RBF and LSW methods could be used for LST calculation. (C) 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.asr.2014.06.011
dc.identifier.endpage1551
dc.identifier.issn0273-1177
dc.identifier.issn1879-1948
dc.identifier.issue8
dc.identifier.scopus2-s2.0-84922581104
dc.identifier.scopusqualityQ1
dc.identifier.startpage1544
dc.identifier.urihttps://doi.org/10.1016/j.asr.2014.06.011
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6372
dc.identifier.volume54
dc.identifier.wosWOS:000343629600008
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofAdvances in Space Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectThermal satellite data
dc.subjectLST
dc.subjectPW
dc.subjectANN
dc.subjectSplit-Window
dc.titleDetermination of land surface temperature using precipitable water based Split-Window and Artificial Neural Network in Turkey
dc.typeArticle

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