A multi-scale wavelet-based temperature and emissivity separation algorithm for hyperspectral thermal infrared data

Abstract

ABSTRACTLand surface temperature and emissivity are essential variables in numerous environmental studies. This article proposes a multi-scale wavelet-based temperature and emissivity separation (MSWTES) algorithm. MSWTES is based on the fact that the high frequencies of ground-leaving radiance and derived emissivity spectra using inaccurate temperature are both closely correlated with the atmospheric downward radiance spectrum. First, surface emissivity can be decomposed by multi-scale wavelet into an optimal level that can be derived from correlation between reconstructed high frequency of ground-leaving radiance and atmospheric downward radiance. Then the ratio of high-frequency energy to low-frequency energy of surface emissivity spectrum is used to measure the degree of atmospheric downward radiance residue in the calculated emissivity spectrum as well as the disparity between the initial surface temperature and the true value. Finally, we can derive the optimal estimate of surface temperature and calculate the surface emissivity spectrum accordingly with this criterion. The MSWTES is first tested by simulation data. When a noise-equivalent spectral error of 2.5 × 10–9 W cm−2 sr−1 cm is considered, the average temperature bias is 0.027 K and the root mean square error (RMSE) of emissivity is less than 0.003, except at the low and high ends of the 750–1250 cm−1 spectral region. Then, the MSWTES is applied to field measurements. As a whole, the MSWTES achieves an RMSE of 0.01 for emissivity retrieval under most conditions, but its accuracy degrades when sample emissivity is extremely low. Meanwhile, the MSWTES is compared to the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm. The performance of the MSWTES is better than that of the ISSTES, which demonstrates the good performance of the MSWTES.