The hyperspectral resolution surface IR emissivity spectra are very important for retrieving products such as dust properties, land surface temperature and cloud-top properties, as well as hyperspectral radiance assimilation overland. The emissivity uncertainty has a significant impact on the retrieval of boundary layer temperature and moisture, especially over the desert regions where surface IR emissivity has large variations both spectrally and spatially. In this study, we have first developed an algorithm to retrieve hyperspectal IR emissivity spectrum simultaneously along with the sounding by using the eigenvector representation of emissivity spectrum derived from laboratory. The method has shown to improve the profile retrievals. Then we have applied this technique to 8-day AIRS radiance measurements and a global hyperspectral resolution surface IR emissivity product has been further derived by composite clear single field of view retrievals. Our derived emissivity agrees with the operational MODIS emissivity product (collection 4) at a few broad spectral bands, but provides a more direct measurement of full spectral information. The spectral and spatial variations of derived emissivity contain useful information on the ecosystem and land surface type properties. They reflect the dramatic differences in International Geosphere-Biosphere Program (IGBP) classification, but show the larger contrasts and more variability in surface types than the IGBP classification.