A variational data assimilation model is developed to estimate surface energy fluxes from remotely sensed land surface temperature (LST). Components of the surface energy balance (sensible, latent, and ground heat fluxes) have different degrees of efficiency in dissipating available energy. LST is the state variable of the surface energy balance (SEB). Land surface models that capture the exchange and storage of energy in the soil and vegetation media use LST as a prognostic variable. Sequences of LST measurements implicitly contain information on partitioning of available energy among the components of SEB. In this study, we focus on the estimation of the sum of the turbulent fluxes as well as the partitioning among them. Two dimensionless parameters are used to characterize the sum and the partitioning. Using LST observations from a constellation of satellites, these parameters are mapped over a large region. The remotely sensed LST is assimilated to the heat diffusion equation within the SEB framework. In addition, a model error term is added to the SEB equation such that the variational data assimilation scheme includes model uncertainty as well as observation error. The framework is tested over the Southern Great Plains region. The mapped results of the surface evaporation estimation are used to study the surface control on evaporation. Independent mapped soil moisture estimates from an airborne microwave campaign are used. The dependence of the evaporation control-soil moisture relationship on vegetation cover and plant functional types over large regions is examined in this first and exploratory study.