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

Abstract:

Monitoring the turbulent heat fluxes and gross primary production (GPP) accurately are vital important for rational utilization of water resources, management decisions, and carbon cycle studies. Land surface temperature (LST) lies in the key role of surface energy balance equation and leaf area index (LAI) is the key role of vegetation dynamics. In this study, LST and LAI observations are assimilated into 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 GPP. The TSEB and VDM are coupled by relating photosynthesis in the VDM to transpiration in the TSEB equation. Within the VDA scheme, four key unknown model parameters are defined and optimized, namely bulk heat transfer coefficient (C_HN), soil evaporative fraction (EFs), canopy evaporative fraction (EFc), and specific leaf area (c_g). The performance of the new 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. Results 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.