Estimation of Turbulent Heat Fluxes and Gross Primary Productivity by Assimilating Land Surface Temperature and Leaf Area Index

Abstract

In this study, land surface temperature (LST) and leaf area index (LAI) observations are merged with a coupled two-source surface energy budget-vegetation dynamic model (TSEB-VDM) via a variational data assimilation (VDA) system to predict turbulent heat fluxes and gross primary productivity (GPP). The TSEB and VDM are coupled by relating photosynthesis in the VDM to transpiration in the TSEB equation. Unknown parameters of the VDA approach are the neutral bulk heat transfer coefficient (C-HN), evaporative fractions for soil and canopy (EFS and EFC), and specific leaf area (c(g)). The VDA approach is evaluated at six AmeriFlux sites with distinct vegetative and climatic characteristics. The modeled sensible (H) and latent (LE) heat fluxes, and GPP agree well with the corresponding eddy covariance measurements in different environmental conditions. The six-site average root mean square error (RMSE) of estimated daily H, LE, and GPP is 42.2 W m(-2), 51.5 W m(-2), and 1.8 gC m(-2) d(-1), respectively. The outcomes show that the developed VDA approach is able to exploit the implicit information in the sequences of LST and LAI measurements to estimate H, LE, and GPP. Our findings also indicate that the estimates of the H and LE are more sensitive to uncertainties in LST measurements, while the GPP retrievals are more affected by uncertainties in the LAI observations.