Temperature and emissivity separation is the key problem in infrared remote sensing.Based on the analysis of the relationship between the atmospheric downward radiance and surface emissivity containing atmosphere residue without the effects of sun irradiation,the present paper puts forward a temperature and emissivity separation algorithm for the ground-based mid-infrared hyperspectral data.The algorithm uses the correlation between the atmospheric downward radiance and surface emissivity containing atmosphere residue as a criterion to optimize the surface temperature,and the correlation between the atmospheric downward radiance and surface emissivity containing atmosphere residue depends on the bias between the estimated surface temperature and true surface temperature.The larger the temperature bias,the greater the correlation.Once we have obtained the surface temperature,the surface emissivity can be calculated easily.The accuracy of the algorithm was evaluated with the simulated mid-infrared hyperspectral data.The results of simulated calculation show that the algorithm can achieve higher accuracy of temperature and emissivity inversion,and also has broad applicability.Meanwhile,the algorithm is insensitive to the instrumental random noise and the change in atmospheric downward radiance during the field measurements.