Use of an ensemble Kalman filter for real-time inversion of leaf area index from MODIS time series data

It is an urgent need for natural disaster monitoring to generate biophysical variables data with high accuracy timely from remotely sensed data. A real-time inversion method to estimate leaf area index (LAI) using MODIS time series reflectance data (MOD09A1) is developed in this paper. A seasonal autoregressive integrated moving average (SARIMA) model is used to derive LAI climatology. A dynamic model is then constructed based on the climatology from the SARIMA model to evolve LAI in time, and used to provide the short-range forecast of LAI. Predictions from the model are used with the ensemble Kalman filter (EnKF) techniques to recursively update biophysical variables as new observations arrive. The validation results show that the real-time inversion method is able to produce a relatively smooth LAI product efficiently, and the accuracy is significantly improved over the MODIS LAI product.