Variational Estimation of Energy Balance Partitioning and Soil Heat Diffusion Using Remotely Sensed Land Surface Temperature Data

Estimates of two major parameters of surface energy balance that control the partitioning of available energy into sensible, latent, and ground heat fluxes are made based on a variational data assimilation (VDA) approach. This method minimizes the estimated soil surface temperature against observations. Sequences of radiometric surface temperature measurements are the only input data source. The one dimensional parabolic heat diffusion equation is used as a physical constraint (the adjoint method). The land data assimilation scheme is formulated such that it does not need ancillary data such as soil texture and vegetation. The two key unknown parameters for estimating fluxes are: near-surface air turbulent conductivity (bulk heat transfer coefficient) and evaporative fraction (ratio of latent heat flux to the sum of the sensible and latent heat fluxes), which is almost constant for near-peak radiation hours. The inclusion of the multi-layer heat diffusion model reduces the phase errors associated with ground heat flux. As a result the new model performs better than the other VDA methods which use parsimonious force-restore equation in place of heat diffusion equation.