Assessing the Performance of Different LAI Data Assimilation Techniques in a Land Surface Model

Accurate estimations of terrestrial carbon-water-energy fluxes can help to understand the complex land-atmosphere interactions. Given the fact that field or remote sensed measurements of these fluxes are either very costly or available only at coarse spatio-temporal resolutions, many studies have investigated techniques of integrating land surface simulations with Earth observations to achieve better estimations. In this study we evaluate the potential of assimilating phenology observations in the Land Information System (LIS) developed by NASA's Goddard Space Flight Center. Specifically, we conducted two feasibility experiments to assimilate synthetic leaf area index (LAI) into the Noah-Multiparameterization Land Surface Model (Noah-MP) using two data assimilation techniques of different complexity: i) a direct insertion algorithm and ii) an ensemble Kalman Filter. Two Observing System Simulation Experiments (OSSEs) are used to compare the efficiency of the two different data assimilation methods. Model surface and root zone soil moisture outputs from the two experiments are compared and evaluated to understand the impact of assimilating vegetation information in land surface modeling.