Derivation of land surface temperature for landsat-8 TIRS using a split window algorithm

Offer Rozenstein; Zhihao Qin; Yevgeny Derimian; Arnon Karnieli

doi:10.3390/s140405768

Derivation of land surface temperature for landsat-8 TIRS using a split window algorithm

Offer Rozenstein, Zhihao Qin, Yevgeny Derimian, Arnon Karnieli

The Swiss Institute for Dryland Environmental and Energy Research

Research output: Contribution to journal › Article › peer-review

341 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	5768-5780
Number of pages	13
Journal	Sensors (Switzerland)
Volume	14
Issue number	4
DOIs	https://doi.org/10.3390/s140405768
State	Published - 25 Mar 2014

Keywords

Land surface temperature
Landsat-8
TIRS
Thermal remote sensing

ASJC Scopus subject areas

Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering

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10.3390/s140405768

Cite this

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abstract = "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.",

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AU - Qin, Zhihao

AU - Derimian, Yevgeny

AU - Karnieli, Arnon

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AB - 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.

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Derivation of land surface temperature for landsat-8 TIRS using a split window algorithm

Abstract

Keywords

ASJC Scopus subject areas

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