In the hyperspectral thermal data analysis temperature-emissivity separation has the same function as reflectance retrieval in the visible and shortwave infrared. The problem however is more complicated since in the thermal the surface emits and reflects radiation. The measured radiance is a function of the materials' surface emissivity and temperature, reflected down welling radiance (clear sky, clouds environment) and the path radiance (temperature and gas (e.g. water vapor, ozone) profiles). The current implementation of the Automatic Retrieval of Temperature and EMIssivity using Spectral Smoothness (ARTEMISS) uses look-up-tables (LUT) to infer the best fitting atmosphere which results in the smallest residual to the In-Scene Atmospheric Compensation (ISAC) estimated transmission. Over last few years we have developed an end-to-end simulation of the hyper spectral exploitation process by generating synthetic data to simulate datasets with "known" ground truth, modeling propagation through the atmosphere, adding sensor effects (telescope, detector, read-out electronics), radiometric and spectral calibration, and test the temperature emissivity separation algorithm. We will present an error analysis where we shifted the band centers, varied the full-width half maximum (FWHM) of the spectral response function, changed the spectral resolution, added noise and varied the atmospheric model. We will also discuss a general method to retrieve the spectral smile as a function of wavelength and the FWHM from hyperspectral data with only approximate spectral calibration. We found that our algorithm has trouble finding a unique solution when the watervapor exceeds about 3 g/cm2 and will discuss remedies for this situation. To speedup the LUT generation we have developed fast and robust initial atmospheric parameter estimators (water vapor, ozone, near surface atmospheric layer temperature) based on channel ratios and brightness temperatures in atmospheric absorption regions for the LWIR.