Sensible and latent heat fluxes are often calculated from bulk transferequations combined with the energy balance. For spatial estimates of thesefluxes, a combination of remotely sensed and standard meteorological datafrom weather stations is used. The success of this approach depends on theaccuracy of the input data and on the accuracy of two variables inparticular: aerodynamic and surface conductance. This paper presents aBayesian approach to improve estimates of sensible and latent heat fluxes byusing a priori estimates of aerodynamic and surface conductance alongsideremote measurements of surface temperature. The method is validated for timeseries of half-hourly measurements in a fully grown maize field, a vineyardand a forest. It is shown that the Bayesian approach yields more accurateestimates of sensible and latent heat flux than traditional methods.
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