Retrieval of LAI by assimilating remotely sensed data into a simple crop growth model

Leaf Area Index (LAI) is an important parameter describing the growth status of vegetation canopy and is also critical to various ecological, biogeochemical and meteorological models. LAI can be conventionally estimated from instantaneous remotely sensed data mainly through Vegetation Indices (VI) and inversion of canopy reflectance models. Data assimilation is a new developed and a promising technique, which can take advantages of time series observations. In this study, the variation algorithm was used to retrieve LAI, by assimilating time series remotely sensed reflectance data into a simple crop growth model, which was obtained by statistical analysis of more than 600 field samples from wheat paddock. To overcome the improper assumption that the other inputs except for LAI in the radiative transfer models are known in data assimilation, we! proposed a strategy to allow the spectral parameters to be free. This strategy was evaluated by simulation. With this method, we also analyzed the influence of background on the retrieved results by simulation. It was further validated using ground measurements. The results were promising compared with field measured LAI data, with the Root-mean-square-error (RMSE) being 0.51.