BRDF modeling and inversion of structure parameters for sparse vegetation canopy

Multi-angular remote sensing became a hot topic after the non-Lambert characteristic of the Earth´s surface had been accepted popularly. A large amount of multi-angular remote sensing data has been obtained with the launch of multi-angle remote sensing sensors. Therefore, modeling of the bi-directional reflectance distribution function (BRDF) for the Earth objects is one of the main subjects at present. A large satellite-airborne-ground synchronous remote sensing experiment was carried out during March 29 to May 10, 2001 at Shunyi, China. The main observation target in this experiment is focused on winter wheat. To describe the BRDF of winter wheat in its early growing stages, we propose a geometric-optical model that is suitable for sparse vegetation, and then try to retrieve the structure parameters based on this model using the field measurements. The purport of the model and its inversion is to inspect the ravages of drought on the wheat just as it is turning green. The winter wheat in our measurement field is sparse and disperses without clear row structures in its turning-green stage. Typical row structure based models and uniform structure based models are not suitable. Our model is developed based on the Li-Strahler geometrical-optical model proposed in 1985. Each cluster of wheat is treated as a hemi-ellipsoid in this model. All of the leaves in the cluster are assumed to cover the hemi-ellipsoid randomly. Leaf area index and leaf angle distribution are two important parameters that are related to the surface area of the hemi-ellipsoid and the leaf distribution on this surface respectively. Leaf angle distribution is also related to the shape of the hemi-ellipsoid. Due to the large uncertainty of the number of hemi-ellipsoids in a unit area, we retrieve this parameter based on our model using the most sensitive samples first, and then treat it as a priori knowledge in the later inversion. The next stage is studying how to use multi-angle remote sensing data to invert vegetation structure parameters.