On the retrival of vegetation parameters from multi-angular hyperspectral remote sensing data

In this study, a new algorithm was developed to effectively use multi-angular hyperspectral remote sensing data in the retrieval of vegetation parameters based on a coupled leaf and canopy reflectance model. Since the observations acquired at different viewing angles tend to have different noise levels, the posterior variance factors of the observations at different angles were estimated and they were then used to construct the observations´ weight factors in model inversion. The developed method was validated using simulated data. The results show that the posterior variance factor can be used to characterize the uncertainty in the data from different sources and thus provides a means to weight these data in the model inversion.