LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages

Leaf area index (LAI) is a basic quantity indicating crop growth situation and plays a significant role in agricultural, ecological and meteorological models at local, regional and global scale. It is a common approach to invert LAI based on canopy reflectance models using optimization method. Radiative transfer model for continuous vegetation canopy such as SAIL models is widely used for crop LAI inversion. However, crops are mostly planted as row structure in China and they donʹt fit the assumptions of continuous vegetation especially at the earlier growth stages. What kind of models should be used to invert LAI for typical row-planted crops at different growing stages? Taking corn as an example, the factors which influence the row planted crop LAI estimation are investigated in this paper. Using the computer simulated BRDF data sets, different models for LAI inversion at different growth stages are evaluated based on parameter sensitivity analysis. Bayes theory is used to introduce a priori knowledge in the inversion process. In 2005, a field campaign is carried out to validate LAI inversion accuracy during cornʹs growing stages in Huailai, Hebei Province, China. Inverted LAI from both the measured Canopy Reflectance (CR) data and Moderate Resolution Imaging Spectroradiometer (MODIS) data are very promising. The results show that at least two kinds of models should be adopted for corn canopy at different growth stages, i.e., row structure model for early growth stage (before elongation) and homogeneous canopy model for later growth stage (after elongation).