Leaf area index inversion using multiangular and multispectral data sets

Leaf area index (LAI) is an important parameter for describing vegetation canopy structure in the terrestrial ecosystem. LAI is closely related to plant transpiration, sunlight intercept, photosynthesis and Net Primary Productivity. Multiangular remote sensing is capable of providing more three-dimension information of vegetation, and it is powerful in solving the problem of the same object with different spectrum or vice versa. As a result, multiangular remote sensing and Bidirectional Reflectance Distribution Function (BRDF) model based inversion may be more suitable for Leaf Area index (LAI) retrieval over row crop canopies. However, it´s still difficult to get LAI without enough a priori knowledge due to the underdetermined problems in inversion. We use the multispectral information to get the a priori estimation of LAI, and then perform BRDF model inversion. Different from the general one channel based BRDF model inversion methods, our new methods use the muiltiangular and multispectral data sets together to increase the available information in inversion, i.e., it is a synthetic method. From the inversion results we found that the new synthetic method is more effective in LAI inversion.