TAISIR, temperature and imaging system infrared, is a nominally satellite based platform for remote sensing of the earth. One of its design features is to acquire atmospheric data simultaneous with ground data, resulting in minimal dependence on external atmospheric models for data correction. Extensive modeling of the rms error of determining a ground temperature and emissivity for a gray body has been performed as a function of integration time, spectroscopic resolution of the system, ground emissivity, atmospheric variables, and atmospheric data accuracy. We find that increased resolution improves measurement accuracy by emphasizing those regions where the atmospheric transmission is highest and atmospheric emission/absorption lowest. We find rms temperature errors <EQ 1 K and rms emissivity errors < 0.01 are obtainable for reasonable seeing and with sufficient information about the atmosphere. A new method is developed for modeling the dependence of the band-averaged transmission and emission. Monte Carlo simulations of satellite data taken using a multi-angle technique are used to derive signal-to-noise requirements. The applicability of those results to the TAISIR system requirements are discussed.