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, the retrieval accuracy still needs to be improved. Through exploring the offset characteristics of atmospheric downward radiance, a temperature and emissivity retrieval algorithm based on atmospheric absorption feature is proposed from hyperspectral thermal infrared data. Furthermore, an optimal channel selection is carried out to improve the efficiency and accuracy of method. The simulated results show that modeling errors less than 0.4K for temperature and 1.5% for relative emissivity for contrast materials and 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 0.5 K and emissivity 2.1%.