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Proceedings Paper

Estimation of land surface emissivity for Landsat TM6 and its application to Lingxian Region in north China
Author(s): Zhihao Qin; Wenjuan Li; Maofang Gao; Hong'ou Zhang
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Paper Abstract

Landsat TM has a thermal band (TM6) operating in 10.45-12.6mm, which can be used for land surface temperature (LST) retrieval. Land surface emissivity (LSE) is an essential parameter for LST retrieval. However, LSE information is generally no available for many applications. In this paper we intend to develop an applicable approach for LSE estimation so that LST can be retrieved from Landsat TM6 data. Spatial resolution of TM6 is 120m under nadir. Pixels under this scale can be viewed as composed of three land cover patterns for most natural surfaces: vegetation, bare soil/rock and water. Emissivities of these land cover patterns are relatively stable and well known, which enables us to propose a method for LSE estimation using the visible and near infrared (NIR) bands. The composition ratio of vegetation and bare soil or building under pixel scale can be estimated from bands 3 and 4 (TM3 and TM4). LSE for TM6 can then be estimated through thermal radiance equation with the composition ratio and the emissivities of the patterns known. The proposed methodology for LSE estimation is simple and easy to use, hence provides opportunity to promote the application of TM6 data to agriculture and environments. Finally we apply this methodology to Lingxian region of Shangdong Province in North China Plain, the most important agricultural region in China, for LSE estimation and LST retrieval, which has produced a reasonable estimation of thermal variation of the region.

Paper Details

Date Published: 3 October 2006
PDF: 8 pages
Proc. SPIE 6366, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI, 636618 (3 October 2006); doi: 10.1117/12.689310
Show Author Affiliations
Zhihao Qin, Chinese Academy of Agricultural Sciences (China)
Nanjing Univ. (China)
Wenjuan Li, Chinese Academy of Agricultural Sciences (China)
Maofang Gao, Chinese Academy of Agricultural Sciences (China)
Hong'ou Zhang, Guangdong Public Lab. of Environmental Science and Technology (China)

Published in SPIE Proceedings Vol. 6366:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI
Manfred Ehlers; Ulrich Michel, Editor(s)

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