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


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


Land Surface Temperature and Emissivity Retrieval from Field-Measured Hyperspectral Thermal Infrared Data Using Wavelet Transform - Download this document for free, or read online. Document in PDF available to download.

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

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





Author: Yu-Ze Zhang 1, Hua Wu 1,2,3,* , Xiao-Guang Jiang 1,2,4,* , Ya-Zhen Jiang 1, Zhao-Xia Liu 5 and Franҫoise Nerry 6

Source: http://mdpi.com/



DOWNLOAD PDF




Related documents