Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data

In this study, several methods to compute land surface temperatures (LST) from Landsat TM5 data are compared. Two different approaches are considered. An image based approach that takes into account atmospherically corrected data by using a dark object subtraction model (DOS-1) and computes the em...

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Main Authors: M. B. Giannini, O. R. Belfiore, C. Parente, R. Santamaria
Format: Article
Language:English
Published: Eastern Macedonia and Thrace Institute of Technology 2015-10-01
Series:Journal of Engineering Science and Technology Review
Subjects:
Online Access:http://www.jestr.org/downloads/Volume8Issue3/fulltext83122015.pdf
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author M. B. Giannini
O. R. Belfiore
C. Parente
R. Santamaria
author_facet M. B. Giannini
O. R. Belfiore
C. Parente
R. Santamaria
author_sort M. B. Giannini
collection DOAJ
description In this study, several methods to compute land surface temperatures (LST) from Landsat TM5 data are compared. Two different approaches are considered. An image based approach that takes into account atmospherically corrected data by using a dark object subtraction model (DOS-1) and computes the emissivity as NDVI function. The emissivity of a surface is controlled by such factors as water content, chemical composition, structure and roughness; it can be determined as the contribution of the different components that belong to the pixels according to their proportions. NDVI method takes into account that vegetation and soils are the main surface cover for the terrestrial component. This emissivity is used to compute the LST by the inversion of Planck function. The other approach applies atmospheric correction to thermal infrared band and considers a constant emissivity of 0.95. Furthermore, the land surface temperature is computed by hybrid methods that result from the merger of the two initially considered approaches. These results are compared with the surface temperature measured by airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS). The LST measured by MIVIS sensor can be considered closer to the real surface temperature because the data are acquired at an altitude of 1500 m and are not affected by significant atmospheric effects such as for satellite data, acquired at 705 km from the Earth’s surface. The best results are obtained by considering variable emissivity.
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spelling doaj.art-cf7b2f01e8e049fdba579f450f0814442022-12-21T17:16:21ZengEastern Macedonia and Thrace Institute of TechnologyJournal of Engineering Science and Technology Review1791-23771791-23772015-10-01838390Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal dataM. B. Giannini0O. R. Belfiore1C. Parente2R. Santamaria3Department of Sciences and Technologies, University of Naples “Parthenope”, Naples, Italy.Department of Sciences and Technologies, University of Naples “Parthenope”, Naples, Italy.Department of Sciences and Technologies, University of Naples “Parthenope”, Naples, Italy.Department of Sciences and Technologies, University of Naples “Parthenope”, Naples, Italy.In this study, several methods to compute land surface temperatures (LST) from Landsat TM5 data are compared. Two different approaches are considered. An image based approach that takes into account atmospherically corrected data by using a dark object subtraction model (DOS-1) and computes the emissivity as NDVI function. The emissivity of a surface is controlled by such factors as water content, chemical composition, structure and roughness; it can be determined as the contribution of the different components that belong to the pixels according to their proportions. NDVI method takes into account that vegetation and soils are the main surface cover for the terrestrial component. This emissivity is used to compute the LST by the inversion of Planck function. The other approach applies atmospheric correction to thermal infrared band and considers a constant emissivity of 0.95. Furthermore, the land surface temperature is computed by hybrid methods that result from the merger of the two initially considered approaches. These results are compared with the surface temperature measured by airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS). The LST measured by MIVIS sensor can be considered closer to the real surface temperature because the data are acquired at an altitude of 1500 m and are not affected by significant atmospheric effects such as for satellite data, acquired at 705 km from the Earth’s surface. The best results are obtained by considering variable emissivity.http://www.jestr.org/downloads/Volume8Issue3/fulltext83122015.pdfRemote SensingLand Surface TemperatureemissivityLandsat 5 TM. _______________________
spellingShingle M. B. Giannini
O. R. Belfiore
C. Parente
R. Santamaria
Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data
Journal of Engineering Science and Technology Review
Remote Sensing
Land Surface Temperature
emissivity
Landsat 5 TM. _______________________
title Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data
title_full Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data
title_fullStr Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data
title_full_unstemmed Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data
title_short Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data
title_sort land surface temperature from landsat 5 tm images comparison of different methods using airborne thermal data
topic Remote Sensing
Land Surface Temperature
emissivity
Landsat 5 TM. _______________________
url http://www.jestr.org/downloads/Volume8Issue3/fulltext83122015.pdf
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