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Journal of Applied Remote Sensing • Open Access

Modeling ground surface temperature by means of remote sensing data in high-altitude areas: test in the central Tibetan Plateau with application of moderate-resolution imaging spectroradiometer Terra/Aqua land surface temperature and ground-based infrared radiometer
Author(s): Defu Zou; Lin Zhao; Tonghua Wu; Xiaodong Wu; Qiangqiang Pang; Zhiwei Wang

Paper Abstract

Ground surface temperature (GST) is a crucial parameter of surface energy budgets and controls the thermal state of the active layer and permafrost in permafrost regions. However, with limited observed datasets available for the Tibetan Plateau, a greater bias existed for GST products from remote sensing data. Model validation (the whole year 2012 data) showed that all three models performed well, with a determination (R2), mean error, mean absolute error, and root mean squared error of 0.86 to 0.93, −0.61 to 1°C, 2.28 to 3.06°C, and 2.96 to 3.83°C, respectively. The model established by observations of Terra and Aqua satellites during the daytime and nighttime showed the highest correlation, with R2 values ranging from 0.91 to 0.93, as well as the lowest MAE and RMSE of 2.28 to 2.42 and 2.96 to 3.05°C, respectively. However, the application of this model substantially reduced the available pixels. Models established with the automatic weather station observations at the satellite overpass times performed better than those using the moderate-resolution imaging spectroradiometer land surface temperature observations. The results might be useful to produce a more reliable dataset for monitoring and modeling permafrost changes.

Paper Details

Date Published: 18 November 2014
PDF: 12 pages
J. Appl. Remote Sens. 8(1) 083516 doi: 10.1117/1.JRS.8.083516
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Defu Zou, Chinese Academy of Sciences (China)
Lin Zhao, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Chinese Academy of Sciences (China)
Tonghua Wu, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Chinese Academy of Sciences (China)
Xiaodong Wu, Chinese Academy of Sciences (China)
Qiangqiang Pang, Chinese Academy of Sciences (China)
Zhiwei Wang, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Chinese Academy of Sciences (China)


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