Estimating Surface Heat Fluxes Using Temperature and Wetness Information: A Particle Data Assimilation Framework

Surface heat fluxes (latent and sensible heat over the land surface) play a key role in the land-atmosphere interaction, and their spatial pattern as well as temporal evolution are vital to the terrestrial water cycle and surface energy balance. Ideally, we want to have accurate estimates of spatially distributed and temporally continuous fluxes. However, this cannot be achieved through interpolation of point measurements because of the limited number of flux stations and the high heterogeneity of fluxes, nor can this be done using large scale monitoring platforms such as remote sensing, since fluxes lack a unique signature that can be detected by satellites. Given the fact that surface heat fluxes are closely related to the thermal and wetness condition of the land surface, which are available from remote sensing instruments, this PhD research proposes a methodology to improve flux estimates by assimilating land surface temperature (LST) and soil wetness information into a coupled water and heat transfer model. The goal is to acquire accurate flux estimates over a large area using a simple model and a small suite of input data...