A methodology to test the pertinence of remote-sensing data assimilation into vegetation models for water and energy exchange at the land surface

This paper presents a methodology to test the performance of assimilation of satellite data into models for the functioning of the continental surface. This methodology applies the Kalman Ensemble Filter to modelling of plant growth and senescence in conjunction with the water and energy exchanges at the land surface. It belongs to a family of methods known in meteorology and oceanography as the Observing System Simulation Experiment (OSSE) approach. By combining information from modelling and observation, the Kalman Ensemble Filter permits corrections in real time of the simulated state of the continental surface, as well as propagation in time of the associated uncertainties. The OSSE approach may present a first step in designing a decision support system, and also in predicting the usefulness of new types of satellite data.