Land surface parameters retrieval using time series remotely sensed observations

Leaf area index (LAI) is an important parameter for estimating growth status of crops. An important method for retrieving LAI from remotely sensed observations is by the canopy reflectance model inversion. But many model inversion methods didn´t take into account the relationship between estimated LAIs in different crop growth stages. In this research, we consider the crop growth model which describe how LAI change with crop growth stages. The main method for this research is assimilating multiple crop´s canopy reflectance observed in different growth stages into the cost function for LAI retrieval. Data assimilation algorithm we used in this study is the variation algorithm. The model inversion results have showed that time series observations of canopy reflectance can decrease uncertainty of estimated LAI in model inversion.