A temperature and emissivity retrieval algorithm based on atmospheric absorption feature from hyperspectral thermal infrared data

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.