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Proceedings Paper

Study on the methods of retrieving urban winter LST based on the Landsat TM 6
Author(s): Jingjing Zhao; Liang Pei; HuiPing Huang; XiaoSong Li
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Paper Abstract

Presently, there are three methods of retrieving land surface temperature based on TM thermal infrared band (band 6). These methods are: the Radiative transfer equation (RTE), the Mono-window algorithm and the General single channel algorithm. Due to the general unavailability of situatmospheric profile data, the RTE method is seldom applied. Though the other two methods are relatively widely researched, the fields are almost limited on the urban hot island study by retrieving hot seasonal temperature. With the increasing emphasis of city energy saving in China and the increasing heating energy consumption, more researchers are beginning to study the construction of city buildings and hot diffusion in winter with remote sensing methods. In order to judge which one of the other two LST retrieval methods is optimal to retrieve cold seasonal temperature, this paper focuses on the retrieval of cold seasonal LST from the Landsat TM6 data. This data was captured on 4th Jan 2007 over the Xi Cheng District in Beijing by using the Mono-window algorithm and the General single channel algorithm. The results indicate that, both methods can produce a similar LST spatial distribution. However, with the higher relative precision, the Mono-window algorithm proved to be more suitable than the General single channel algorithm to this research area. Furthermore, the author presents some suggestions on the choice of winter temperature LST methods.

Paper Details

Date Published: 13 October 2009
PDF: 7 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74920K (13 October 2009); doi: 10.1117/12.838339
Show Author Affiliations
Jingjing Zhao, Liaoning Technical Univ. (China)
The Institute of Remote Sensing Application (China)
Liang Pei, Liaoning Technical Univ. (China)
HuiPing Huang, The Institute of Remote Sensing Application (China)
XiaoSong Li, The Institute of Remote Sensing Application (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

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