CHEN Meng-Shuo
Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of SciencesQIAN Yong-Gang
Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of SciencesWang Ning
Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of SciencesMA Ling-Ling
Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of SciencesLi Chuan-Rong
Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of SciencesTang Ling-Li
Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of SciencesKey Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences,Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences,Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences,Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences,Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences,Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences
Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared (TIR) remote sensing. However, because of the ill-posed problem and the at-ground radiance’s coupling with atmospheric radiance, the retrieval accuracy still needs to be improved. Through exploring the offset characteristic of atmospheric downward radiance, a temperature and emissivity retrieval algorithm based on atmospheric absorption feature was proposed from hyperspectral thermal infrared data by assuming that the land surface emissivity is equal between the adjacent channels. Furthermore, an optimal selection of channels was carried out to improve the efficiency and accuracy of method. The proposed method can reduce the influence of atmospheric correction error. The simulated results show that the accuracy is similar to the ISSTES method (Borel, 2008) for high emissivity materials. Furthermore, the proposed method can enhance the retrieval accuracy for low emissivity materials, that is approximately temperature 0.48 K and emissivity 2.1%. The results from the field measured data show that about 77% of the samples have an accuracy of LST within 1.0 K with the mean of LSEs lower than 0.01.
CHEN Meng-Shuo, QIAN Yong-Gang, Wang Ning, MA Ling-Ling, Li Chuan-Rong, Tang Ling-Li. A temperature and emissivity retrieval algorithm based on atmospheric absorption feature from hyperspectral thermal infrared data[J]. Journal of Infrared and Millimeter Waves,2016,35(5):617~624
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