A data-based mechanistic assimilation method to estimate time series LAI

In recent years, time series remote sensing data products have been assimilated into the coupled crop growth model and the radiative transfer model to improve the time series LAI estimation. However, due to the large number of input parameters to the crop growth model, the applications of the crop growth models for regional use is restricted. This paper proposed a data-based mechanistic assimilation method for estimation of the time series LAI from Moderate Resolution Imaging Spectroradiometer (MODIS) data. By coupling a revised universal data-based mechanistic model (LAI_UDBM) with a vegetation canopy radiative transfer model (PROSAIL), The proposed method applies the Ensemble Kalman Filter (ENKF) method to improve the estimation accuracy. Results indicate that the time series LAI estimated by this approach is superior to the MODIS LAI. Furthermore, because the model does not require the historical observation of every pixel, it is applicable over a wider range of uses.