We present a method for atmospheric estimation in hyperspectral longwave infrared (LWIR) data. The methods also involves the estimation of object parameters (temperature and emissivity) under the restriction that the emissivity is constant for all wavelenghts. The method is analyzed with respect to its sensitivity to noise and number of spectral bands. Simulations with synthetic signatures and signatures from real vegetation are performed to validate the analysis. However, the simulations do not yet include a realistic model of sensor noise. Several issues for further studies have been identifies, among them the need for more realistic sensor models, robust estimation using multiple observations, and the continuation towards temperature-emissivity separation (TES) and material classification. In conclusion, the proposed method allows estimation with as few as 10-20 spectral bands at moderate noise levels. More than 20 bands does not improve the estimates.