One of the most important Earth System Data Records (ESDR’s) identified by NASA and numerous international organizations is Land Surface Temperature and Emissivity (LST&E) [King, 1999]. LST&E data are key parameters in the physics of climate modeling, ice dynamic analyses, surface-atmosphere interactions and land use, land cover change. For example, emissivity is a critical component for climate and ecosystem models that determine surface radiation budget and energy flux calculations between the surface and the atmosphere. Furthermore, knowledge of the surface emissivity is needed to recover the Land Surface Temperature (LST), an important climate variable in many scientific studies from climatology to hydrology and modeling the greenhouse effect. Sensitivity tests indicate that a decrease of soil emissivity by 0.1 will result in current climate models having errors of up to 6.6 Wm^2 in upward longwave radiation for their surface energy budget in arid and semi-arid regions [Zhou et al., 2003; Jin and Liang, 2006]. This represents a much larger term than, for example, surface radiative forcing due to greenhouse gases. The atmospheric retrieval community and numerical weather prediction operational centers are expected to benefit from an improved emissivity product since currently they frequently use constant or inaccurate surface emissivities that typically result in large temperature and moisture profile errors, particularly over desert and semi-arid regions where the variation in emissivity is both large spatially and spectrally [Li et al., 2007]. By producing a more accurate LST&E products, along with an error estimates, the community aims to minimize what would normally be a major source of error and bias in retrieval schemes and the use of satellite radiances in data assimilation.