Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm

Land surface temperature (LST) is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS). This paper presents an adjustment of the split window algorithm (SWA) for TIRS that uses a...

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Main Authors: Offer Rozenstein, Zhihao Qin, Yevgeny Derimian, Arnon Karnieli
Format: Article
Language:English
Published: MDPI AG 2014-03-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/14/4/5768
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spelling doaj-13deb0f74f6842efa2e0a986306766af2020-11-24T21:47:20ZengMDPI AGSensors1424-82202014-03-011445768578010.3390/s140405768s140405768Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window AlgorithmOffer Rozenstein0Zhihao Qin1Yevgeny Derimian2Arnon Karnieli3The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus, Midreshet Ben-Gurion 84990, IsraelInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaLaboratoire d'Optique Atmosphérique, Université de Lille1/CNRS, Villeneuve d'Ascq 59655, FranceThe Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus, Midreshet Ben-Gurion 84990, IsraelLand surface temperature (LST) is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS). This paper presents an adjustment of the split window algorithm (SWA) for TIRS that uses atmospheric transmittance and land surface emissivity (LSE) as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE) of the simulated LST was calculated as 0.93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS.http://www.mdpi.com/1424-8220/14/4/5768thermal remote sensingTIRSLandsat-8land surface temperature
collection DOAJ
language English
format Article
sources DOAJ
author Offer Rozenstein
Zhihao Qin
Yevgeny Derimian
Arnon Karnieli
spellingShingle Offer Rozenstein
Zhihao Qin
Yevgeny Derimian
Arnon Karnieli
Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm
Sensors
thermal remote sensing
TIRS
Landsat-8
land surface temperature
author_facet Offer Rozenstein
Zhihao Qin
Yevgeny Derimian
Arnon Karnieli
author_sort Offer Rozenstein
title Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm
title_short Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm
title_full Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm
title_fullStr Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm
title_full_unstemmed Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm
title_sort derivation of land surface temperature for landsat-8 tirs using a split window algorithm
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2014-03-01
description Land surface temperature (LST) is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS). This paper presents an adjustment of the split window algorithm (SWA) for TIRS that uses atmospheric transmittance and land surface emissivity (LSE) as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE) of the simulated LST was calculated as 0.93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS.
topic thermal remote sensing
TIRS
Landsat-8
land surface temperature
url http://www.mdpi.com/1424-8220/14/4/5768
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AT zhihaoqin derivationoflandsurfacetemperatureforlandsat8tirsusingasplitwindowalgorithm
AT yevgenyderimian derivationoflandsurfacetemperatureforlandsat8tirsusingasplitwindowalgorithm
AT arnonkarnieli derivationoflandsurfacetemperatureforlandsat8tirsusingasplitwindowalgorithm
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