A methodology for computing spatially and temporally varying surface sensible heat flux from thermal imagery

Thermal infrared imagery provides a measure of surface temperature by quantifying the thermal emission of radiation from objects. In this work, we propose a novel approach for computing surface sensible heat fluxes from a time series of thermal imagery sampled at 20 Hz. The approach consists of using a surface energy budget, where the ground heat flux is easily computed from limited measurements using a force restore-type methodology, the latent heat fluxes are neglected, and the energy storage is computed using a lumped capacitance model. Preliminary validation of the method is presented using experimental data acquired at a desert playa in western Utah from an intensive observation period during the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program. The sensible heat fluxes computed from thermal imagery show encouraging agreement (approximate to 4% difference for 1 h averaged fluxes computed with the force-restore method) compared to heat fluxes obtained using the eddy-covariance technique, however additional evaluation is required to confirm the method's validity. Further, the technique illustrates the richness of surface heat flux spatio-temporal structure even over very smooth surfaces.