An ensemble multiscale filter approach for retrieving leaf area index from multiple satellite data

High-quality leaf area index (LAI) products retrieved from satellite observations are urgently needed for crop growth monitoring and yield estimation, land-surface process simulation and global change studies. Currently many LAI products are produced from different satellites. However, those products are usually limited to certain resolutions and some of them are incomplete in space. So these products cannot meet the requirements of practical applications. In this study, an ensemble multiscale filer (EnMsF) coupled with the radiative transfer model, is used to retrieve LAI from multi-sensor reflectance data with different spatial resolution. The result shows the approach can produce optimal estimation of LAI at any desired spatial scale given sparse observations at different scales.