Downscaling of the Land Surface Temperature Data Obtained at four Different Dates in a Year Using the GWR Model: A Case Study in Antakya, Turkey

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dc.authoridOZBULDU, MUSTAFA/0000-0002-5359-8750
dc.authoridIRVEM, AHMET/0000-0002-3838-1924
dc.contributor.authorIrvem, Ahmet
dc.contributor.authorOzbuldu, Mustafa
dc.date.accessioned2024-09-18T20:25:20Z
dc.date.available2024-09-18T20:25:20Z
dc.date.issued2023
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractLand surface temperature (LST) is a major factor that affects many biophysical processes in the land-atmosphere relationship. This factor is obtained from satellite images having different temporal and spatial resolutions. This study applied the geographically weighted regression (GWR) model for four different dates representing each season a year to improve the LST images obtained in coarse resolution. In this study, MODIS LST images that are available having fine temporal but coarse spatial resolution were modeled using NDBI and NDVI indices, and their spatial resolution is improved. In addition, LANDSAT 8 images were used as reference images to evaluate the accuracy of the images obtained from the models. Results of the GWR model have been evaluated by comparing it statistically with TsHARP and DisTradother commonly used methods. As a result of the comparison by using the average of four dates outputs, the GWR model (R-2 = 0.73, RMSE = 0.78) was more successful than the TsHARP (R-2 = 0.56, RMSE = 1.00) and DisTrad (R-2 = 0.49, RMSE = 1.09) methods. The most successful downscaling performance in the GWR model was obtained in the spring season (RSR = 0.48). According to these findings, the GWR model can be used for downscaling LST images in urban areas. However, before applying this algorithm to scenarios outside of urban areas, it is recommended to use the required parameters and optimize their combinations.en_US
dc.identifier.doi10.1007/s12524-023-01700-5
dc.identifier.endpage1252en_US
dc.identifier.issn0255-660X
dc.identifier.issn0974-3006
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85153719556en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1241en_US
dc.identifier.urihttps://doi.org/10.1007/s12524-023-01700-5
dc.identifier.urihttps://hdl.handle.net/20.500.12483/10252
dc.identifier.volume51en_US
dc.identifier.wosWOS:000978518100001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of The Indian Society of Remote Sensingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGeographically weighted regressionen_US
dc.subjectTsHARPen_US
dc.subjectDisTraden_US
dc.subjectLSTen_US
dc.subjectDownscalingen_US
dc.titleDownscaling of the Land Surface Temperature Data Obtained at four Different Dates in a Year Using the GWR Model: A Case Study in Antakya, Turkeyen_US
dc.typeArticleen_US

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