  An approach to the remote sensing of land surface temperature is developed
using the methods of Bayesian inference. The starting point is the maximum
entropy estimate for the posterior distribution of radiance in multiple bands.
In order to convert this quantity to an estimator for surface temperature and
emissivity with Bayes' theorem, it is necessary to obtain the joint prior
probability for surface temperature and emissivity, given available prior
knowledge. The requirement that any pair of distinct observers be able to
relate their descriptions of radiance under arbitrary Lorentz transformations
uniquely determines the prior probability. Perhaps surprisingly, surface
temperature acts as a scale parameter, while emissivity acts as a location
parameter, giving the prior probability P(T,emissivity|K)=const./T dT
d(emissivity). Given this result, it is a simple matter to construct estimators
for surface temperature and emssivity. Monte Carlo simulations of land surface
temeprature retrieval in selected MODIS bands are presented as examples of the
utility of the approach.
