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2019-09-08Zeitschriftenartikel DOI: 10.18452/20952
A PCA−OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations
dc.contributor.authorkarimi firozjaei, mohammad
dc.contributor.authorAlavipanah, Seyed Kazem
dc.contributor.authorLiu, Hua
dc.contributor.authorSedighi, Amir
dc.contributor.authorMijani, Naeim
dc.contributor.authorKiavarz Moghaddam, Majid
dc.contributor.authorWeng, Qihao
dc.date.accessioned2019-12-17T10:38:47Z
dc.date.available2019-12-17T10:38:47Z
dc.date.issued2019-09-08none
dc.date.updated2019-10-08T10:01:04Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/21704
dc.description.abstractAnalysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectLST variationeng
dc.subjectsurface biophysical parameterseng
dc.subjectPCAeng
dc.subjectOLS regressioneng
dc.subjectregional and local optimizationeng
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitennone
dc.titleA PCA−OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variationsnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/21704-8
dc.identifier.doihttp://dx.doi.org/10.18452/20952
dc.type.versionpublishedVersionnone
local.edoc.pages22none
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
dc.description.versionPeer Reviewednone
dc.identifier.eissn2072-4292
dcterms.bibliographicCitation.doi10.3390/rs11182094none
dcterms.bibliographicCitation.journaltitleRemote Sensingnone
dcterms.bibliographicCitation.volume11none
dcterms.bibliographicCitation.issue18none
dcterms.bibliographicCitation.articlenumber2094none
dcterms.bibliographicCitation.originalpublishernameMDPInone
dcterms.bibliographicCitation.originalpublisherplaceBaselnone
bua.import.affiliationFirozjaei, Mohammad Karimi; Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran, Department of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany,none
bua.import.affiliationAlavipanah, Seyed Kazem; Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran, Department of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany,none
bua.import.affiliationLiu, Hua; Department of Political Science and Geography, Old Dominion University, Norfolk, VA 23529, USA,none
bua.import.affiliationSedighi, Amir; Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran,none
bua.import.affiliationMijani, Naeim; Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran,none
bua.import.affiliationKiavarz, Majid; Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran,none
bua.import.affiliationWeng, Qihao; Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA,none
bua.departmentMathematisch-Naturwissenschaftliche Fakultätnone

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