<rdf:RDF xmlns:rdf="http://www.openarchives.org/OAI/2.0/rdf/" xmlns:ow="http://www.ontoweb.org/ontology/1#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:ds="http://dspace.org/ds/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/rdf/ http://www.openarchives.org/OAI/2.0/rdf.xsd">
   <ow:Publication rdf:about="oai:digibug.ugr.es:10481/68076">
      <dc:title>Spatial and Multi-Temporal Analysis of Land Surface Temperature through Landsat 8 Images: Comparison of Algorithms in a Highly Polluted City (Granada)</dc:title>
      <dc:creator>Hidalgo García, David</dc:creator>
      <dc:creator>Arco Díaz, Julián</dc:creator>
      <dc:subject>Landsat 8 images</dc:subject>
      <dc:subject>Panel data analysis</dc:subject>
      <dc:subject>Land surface temperature</dc:subject>
      <dc:subject>Land surface temperature</dc:subject>
      <dc:description>Over the past decade, satellite imaging has become a habitual way to determine the&#xd;
land surface temperature (LST). One means entails the use of Landsat 8 images, for which mono&#xd;
window (MW), single channel (SC) and split window (SW) algorithms are needed. Knowing the&#xd;
precision and seasonal variability of the LST can improve urban climate alteration studies, which&#xd;
ultimately help make sustainable decisions in terms of the greater resilience of cities. In this study&#xd;
we determine the LST of a mid-sized city, Granada (Spain), applying six Landsat 8 algorithms that&#xd;
are validated using ambient temperatures. In addition to having a unique geographical location, this&#xd;
city has high pollution and high daily temperature variations, so that it is a very appropriate site for&#xd;
study. Altogether, 11 images with very low cloudiness were taken into account, distributed between&#xd;
November 2019 and October 2020. After data validation by means of R2&#xd;
statistical analysis, the root&#xd;
mean square error (RMSE), mean bias error (MBE) and standard deviation (SD) were determined to&#xd;
obtain the coefficients of correlation. Panel data analysis is presented as a novel element with respect&#xd;
to the methods usually used. Results reveal that the SC algorithms prove more effective and reliable&#xd;
in determining the LST of the city studied here.</dc:description>
      <dc:date>2021-04-23T10:25:45Z</dc:date>
      <dc:date>2021-04-23T10:25:45Z</dc:date>
      <dc:date>2021</dc:date>
      <dc:type>info:eu-repo/semantics/article</dc:type>
      <dc:identifier>Hidalgo García, D.; Arco Díaz, J. Spatial and Multi-Temporal Analysis of Land Surface Temperature through Landsat 8 Images: Comparison of Algorithms in a Highly Polluted City (Granada). Remote Sens. 2021, 13, 1012. https:// doi.org/10.3390/rs13051012</dc:identifier>
      <dc:identifier>http://hdl.handle.net/10481/68076</dc:identifier>
      <dc:identifier>10.3390/rs13051012</dc:identifier>
      <dc:language>eng</dc:language>
      <dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights>
      <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
      <dc:rights>Atribución 3.0 España</dc:rights>
      <dc:publisher>MDPI</dc:publisher>
   </ow:Publication>
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