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Remote Sens. 2017, 9(5), 454; doi:10.3390/rs9050454

Land Surface Temperature and Emissivity Retrieval from Field-Measured Hyperspectral Thermal Infrared Data Using Wavelet Transform

1
University of Chinese Academy of Sciences, Beijing 100049, China
2
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China
5
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
6
ICube, UdS, CNRS, 300 Bld Sébastien Brant, CS10413, 67412 Illkirch, France
*
Authors to whom correspondence should be addressed.
Academic Editors: Zhaoliang Li, Bo-Hui Tang and Prasad S. Thenkabail
Received: 9 March 2017 / Revised: 21 April 2017 / Accepted: 3 May 2017 / Published: 7 May 2017
View Full-Text   |   Download PDF [3756 KB, uploaded 7 May 2017]   |  

Abstract

Currently, the main difficulty in separating the land surface temperature (LST) and land surface emissivity (LSE) from field-measured hyperspectral Thermal Infrared (TIR) data lies in solving the radiative transfer equation (RTE). Based on the theory of wavelet transform (WT), this paper proposes a method for accurately and effectively separating LSTs and LSEs from field-measured hyperspectral TIR data. We show that the number of unknowns in the RTE can be reduced by decomposing and reconstructing the LSE spectrum, thus making the RTE solvable. The final results show that the errors introduced by WT are negligible. In addition, the proposed method usually achieves a greater accuracy in a wet-warm atmosphere than that in a dry-cold atmosphere. For the results under instrument noise conditions (NE∆T = 0.2 K), the overall accuracy of the LST is approximately 0.1–0.3 K, while the Root Mean Square Error (RMSE) of the LSEs is less than 0.01. In contrast to the effects of instrument noise, our method is quite insensitive to noises from atmospheric downwelling radiance, and all the RMSEs of our method are approximately zero for both the LSTs and the LSEs. When we used field-measured data to better evaluate our method’s performance, the results showed that the RMSEs of the LSTs and LSEs were approximately 1.1 K and 0.01, respectively. The results from both simulated data and field-measured data demonstrate that our method is promising for decreasing the number of unknowns in the RTE. Furthermore, the proposed method overcomes some known limitations of current algorithms, such as singular values and the loss of continuity in the spectrum of the retrieved LSEs. View Full-Text
Keywords: temperature and emissivity separation; hyperspectral; field-measured data; wavelet transform temperature and emissivity separation; hyperspectral; field-measured data; wavelet transform
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Zhang, Y.-Z.; Wu, H.; Jiang, X.-G.; Jiang, Y.-Z.; Liu, Z.-X.; Nerry, F. Land Surface Temperature and Emissivity Retrieval from Field-Measured Hyperspectral Thermal Infrared Data Using Wavelet Transform. Remote Sens. 2017, 9, 454.

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