Influence of sensor spectral properties on temperature and emissivity separation for hyperspectral thermal infrared data

Abstract

In this paper, different temperature and emissivity separation (TES) for hyperspectral thermal infrared data, including iterative spectrally smooth temperature-emissivity separation method (ISSTES), automatic retrieval of temperature and emissivity using spectral smoothness method (ARTEMISS), spectral smoothness method (SpSm), downwelling radiance residual index method (DRRI), and linear spectral emissivity constraint method (LSEC). The performances of these TES methods are analyzed and compared under different instrument characteristics, including spectral shifting and full-width half-maximum (FWHM) broadening and instrument noise with the simulated data. The results show the spectral smoothness methods all have similar sensitivities to instrument characteristics, but the definition of smoothness indices is an important factor that affects the retrieval accuracy. LSEC has the most robust and accurate performance, even with a spectral shifting of 0.1 cm−1, FWHM broadening of 20% and instrument noise of 0.2K, the RMSE of retrieved temperature is still within 0.15K.