Temperature and emissivity retrieval from low-emissivity materials using hyperspectral thermal infrared data

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared remote sensing. Many TES algorithms have been proposed and validated on natural surface materials such as water or vegetation. However, when those algorithms are applied to low-emissivity materials such as some metals, the retrieval accuracy needs significant improvement. Here, we propose a new temperature and emissivity retrieval algorithm for hyperspectral thermal infrared data based on bias characteristic of atmospheric downward radiance. This approach is aimed at weakening the large influence of errors in atmospheric downward radiance on the TES of low-emissivity materials due to stronger coupling between the surface and atmosphere. Compared with traditional approaches such as the iterative spectrally smooth temperature and emissivity separation (ISSTES) method, the proposed method improves retrieval accuracy for low-emissivity materials by about 0.3, 0.4, and 1K for three low-emissivity test materials (galvanized steel metal, metallic silver paint, and gold paint sandpaper). Thus, the proposed algorithm can weaken the influence of atmospheric downward radiance error. In addition, the computation efficiency is higher than ISSTES because fewer groups of channels are involved in the calculation.