Estimation of Surface Turbulent Fluxes from Land Surface Moisture and Temperature via a Variational Data Assimilation Framework

Accurate estimation of surface turbulent heat fluxes is important in numerous hydrological, meteorological, and agricultural applications. Recently, several studies have focused on estimating these fluxes via assimilation of land surface temperature (LST) observations into a surface energy balance model following the variational data assimilation (VDA) scheme. However, current VDAs suffer from the following issues: (1) they do not consider the inherent coupling between water and energy in the soil-plant-atmosphere continuum, (2) they tend to be ill-posed, and (3) they do not explicitly compute the uncertainty of estimates. The goal of this study is to enhance the current VDAs in two major ways: (i) coupling water and energy balance equations, assimilating soil moisture (SM) data in addition to LST, and constraining the VDA estimates by the moisture diffusion equation in addition to the heat diffusion equation; and (ii) analyzing the second-order information that guides toward a well-posed estimation problem and provides uncertainty of parameters. The performance of the proposed VDA is examined through a set of experiments based on a synthetic data set. The results show that simultaneous assimilation of SM and LST improves the estimation of heat fluxes and reduces the sensitivity of VDA to the initial guess of parameters. Furthermore, by adding moisture diffusion equation as an additional constraint, the correlation between the estimated parameters is reduced and the VDA scheme is oriented toward well posedness. The feasibility of extending the proposed VDA in estimating large-scale turbulent fluxes using spaceborne SM and LST data is examined, and promising results are obtained.