Assimilation of satellite reflectance data into a dynamical leaf model to infer seasonally varying leaf areas for climate and carbon models

Leaf area index is an important input for many climate and carbon models. The widely used leaf area products derived from satellite-observed surface reflectances contain substantial erratic fluctuations in time due to inadequate atmospheric corrections and observational and retrieval uncertainties. These fluctuations are inconsistent with the seasonal dynamics of leaf area, known to be gradual. Their use in process-based terrestrial carbon models corrupts model behavior, making diagnosis of model performance difficult. We propose a data assimilation approach that combines the satellite observations of Moderate Resolution Imaging Spectroradiometer ( MODIS) albedo with a dynamical leaf model. Its novelty is that the seasonal cycle of the directly retrieved leaf areas is smooth and consistent with both observations and current understandings of processes controlling leaf area dynamics. The approach optimizes the dynamical model parameters such that the difference between the estimated surface reflectances based on the modeled leaf area and those of satellite observations is minimized. We demonstrate the usefulness and advantage of our new approach at multiple deciduous forest sites in the United States.