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Comparison of four algorithms to retrieve land surface temperature using Landsat 8 satellite

Song, Ting ; Duan, Zheng LU ; Liu, Junzhi ; Shi, Junzhe ; Yan, Fei ; Sheng, Shijie ; Huang, Jun and Wu, Wei (2015) In Yaogan Xuebao/Journal of Remote Sensing 19(3). p.451-464
Abstract

Land Surface Temperature (LST) plays an important role in energy exchange between the land surface and the atmosphere. LST is a key variable in many applications, such as land surface modeling. Many satellite-based algorithms have been proposed to retrieve LST, such as Split-Window (SW), dual-angle, and single-channel algorithms. In this study, four satellite-based LST retrieval algorithms, including two SW algorithms (Juan C. Jiménez-Muñoz and Offer Rozenstein SW algorithms) and two mono-window algorithms (Juan C. Jiménez-Muñoz and Qin Zhihao mono-window algorithms), were compared with Landsat-8 satellite data over the region around Wuxi City. The accuracy of the four algorithms was evaluated against the ground measurements from 16... (More)

Land Surface Temperature (LST) plays an important role in energy exchange between the land surface and the atmosphere. LST is a key variable in many applications, such as land surface modeling. Many satellite-based algorithms have been proposed to retrieve LST, such as Split-Window (SW), dual-angle, and single-channel algorithms. In this study, four satellite-based LST retrieval algorithms, including two SW algorithms (Juan C. Jiménez-Muñoz and Offer Rozenstein SW algorithms) and two mono-window algorithms (Juan C. Jiménez-Muñoz and Qin Zhihao mono-window algorithms), were compared with Landsat-8 satellite data over the region around Wuxi City. The accuracy of the four algorithms was evaluated against the ground measurements from 16 floating stations over Lake Tai. The results showed that the performance of the two SW algorithms, which have an average error of 0.7 K, was better than that of the SW algorithms, which have an average error of 1.3-1.4 K, when compared with ground measurements. The sensitivity analysis of these algorithms showed that the Juan C. Jiménez-Muñoz SW algorithm was the least sensitive to key input parameters (emissivity and water vapor), whereas the Offer Rozenstein SW algorithm and the Qin Zhihao mono-window algorithm showed high sensitivity to input parameters. The limitations of these four LST retrieving algorithms were also discussed.

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author
; ; ; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Land surface temperature, Landsat 8, Mono-window algorithm, Split-window algorithm, Thermal infrared
in
Yaogan Xuebao/Journal of Remote Sensing
volume
19
issue
3
pages
14 pages
publisher
Science Press
external identifiers
  • scopus:84935059910
ISSN
1007-4619
language
English
LU publication?
no
id
132ed174-778e-4d1a-a6ae-0fad35f26453
date added to LUP
2019-12-22 20:28:13
date last changed
2022-03-26 01:06:20
@article{132ed174-778e-4d1a-a6ae-0fad35f26453,
  abstract     = {{<p>Land Surface Temperature (LST) plays an important role in energy exchange between the land surface and the atmosphere. LST is a key variable in many applications, such as land surface modeling. Many satellite-based algorithms have been proposed to retrieve LST, such as Split-Window (SW), dual-angle, and single-channel algorithms. In this study, four satellite-based LST retrieval algorithms, including two SW algorithms (Juan C. Jiménez-Muñoz and Offer Rozenstein SW algorithms) and two mono-window algorithms (Juan C. Jiménez-Muñoz and Qin Zhihao mono-window algorithms), were compared with Landsat-8 satellite data over the region around Wuxi City. The accuracy of the four algorithms was evaluated against the ground measurements from 16 floating stations over Lake Tai. The results showed that the performance of the two SW algorithms, which have an average error of 0.7 K, was better than that of the SW algorithms, which have an average error of 1.3-1.4 K, when compared with ground measurements. The sensitivity analysis of these algorithms showed that the Juan C. Jiménez-Muñoz SW algorithm was the least sensitive to key input parameters (emissivity and water vapor), whereas the Offer Rozenstein SW algorithm and the Qin Zhihao mono-window algorithm showed high sensitivity to input parameters. The limitations of these four LST retrieving algorithms were also discussed.</p>}},
  author       = {{Song, Ting and Duan, Zheng and Liu, Junzhi and Shi, Junzhe and Yan, Fei and Sheng, Shijie and Huang, Jun and Wu, Wei}},
  issn         = {{1007-4619}},
  keywords     = {{Land surface temperature; Landsat 8; Mono-window algorithm; Split-window algorithm; Thermal infrared}},
  language     = {{eng}},
  month        = {{05}},
  number       = {{3}},
  pages        = {{451--464}},
  publisher    = {{Science Press}},
  series       = {{Yaogan Xuebao/Journal of Remote Sensing}},
  title        = {{Comparison of four algorithms to retrieve land surface temperature using Landsat 8 satellite}},
  volume       = {{19}},
  year         = {{2015}},
}