Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery

Abstract

Turbulent heat fluxes from the surface do not have a unique signature that can be detected by remotely deployed instruments. In order to retrieve the fluxes, the measurements need to be merged into models that infer fluxes from their space and time patterns. This study is based on variational assimilation of land surface temperature (LST) into a surface energy balance model with dual‐source soil and vegetation flux components. There are two major unknown parameters in the estimation of land evaporation: near‐surface air turbulent conductivity (that scales the magnitude of the fluxes) and evaporative fraction (that partitions the total flux into latent and sensible heat flux). This study advances the data assimilation approach in two major new directions. First and foremost, it recasts the variational assimilation system as a multiscale problem with LST estimates from a constellation of satellites. The assimilation system can ingest measurements with varying scales and overlapping coverages. Second, the remotely sensed LST is treated as a combination of contributions from the canopy and the exposed soil surface. Application to a large area within the U.S. Great Plains is shown. Spatial patterns of the retrieved parameters, their correspondence to observed land use maps, and their consistency with seasonal phenology are demonstrated. Finally, the performance of a combined‐source formulation is compared with the dual‐source model. Remarkably, the spatial patterns of the heat transfer coefficient reflect dominant vegetation patterns, even though there was no vegetation index information used in the combined‐source formulation.