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Correction published on 24 June 2014, see Sensors 2014, 14(6), 11277.
Open AccessArticle

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

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The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus, Midreshet Ben-Gurion 84990, Israel
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Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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Laboratoire d'Optique Atmosphérique, Université de Lille1/CNRS, Villeneuve d'Ascq 59655, France
*
Author to whom correspondence should be addressed.
Sensors 2014, 14(4), 5768-5780; https://doi.org/10.3390/s140405768
Received: 7 January 2014 / Revised: 10 February 2014 / Accepted: 15 March 2014 / Published: 25 March 2014
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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. View Full-Text
Keywords: thermal remote sensing; TIRS; Landsat-8; land surface temperature thermal remote sensing; TIRS; Landsat-8; land surface temperature
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MDPI and ACS Style

Rozenstein, O.; Qin, Z.; Derimian, Y.; Karnieli, A. Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm. Sensors 2014, 14, 5768-5780.

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