A Multiscale Assimilation Approach to Improve Fine-Resolution Leaf Area Index Dynamics

Fine spatial details of vegetation growth are usually lost in leaf area index (LAI) products obtained from coarse spatial resolution satellite sensors. This may bring uncertainties in ecosystem process models, which usually require LAI products with fine spatiotemporal resolutions. Successful downscaling of LAI dynamics to fine spatial resolution is very important for meeting the demands of these models. Hence, a multiscale multisensor approach using the ensemble Kalman smoother (EnKS) technique is proposed in this paper. The LAI dynamics at a coarser spatial resolution are incorporated as prior information into the remotely sensed observations for time series LAI estimation at a finer spatial resolution. Downscaled LAI dynamics are evaluated based on spatial distribution and temporal trajectory. The results indicate the assimilated LAI to be in good agreement with the reference values at the different spatial scales. For example, the coefficient of determination ( $R^{2}$ ) between the reference values and fine-resolution LAI results retrieved by the proposed approach is 0.71 with a root-mean-square-error (RMSE) value of 0.65 on Julian day 185 at the Agro site. The method has proved to be effective for downscaling LAI dynamics, which improves the spatiotemporal patterns of fine-resolution LAI retrievals with respect to earlier methods.