Performances of temperature and emissivity separation methods for hyperspectral thermal data affected by the changes of spectral properties of sensor

In this paper, great efforts are focused on the temperature and emissivity separation (TES) from hyperspectral thermal infrared data. However, instead of proposing new method, the performances of several published TES methods, 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) are analyzed under different instrument characteristics, including the shifting of spectral and the broadening of the full-width half-maximum (FWHM), with the simulated data. The results shows that LSEC has the most robust and accurate performance. DRRI also has a good performance, but a channel selection procedure is required before the use of this method. ISSTES, ARTEMISS and SpSm are more sensitive to the instrument characteristics with some larger errors than other two methods.