Kernel - based vegetation index and its validation with different-scale BRDF data sets

Abstract

Traditional vegetation indices are usually constructed by using red and near-infrared band reflectance data under single solar incidence-observation geometry. However, because of the anisotropy of the Earth surface's reflectance, vegetation indices acquired from different solar-incidence observation geometries exhibit lots of variances. Meanwhile, most of those indices only utilize vegetation's spectral information, and anisotropic reflectance of vegetation is considered as a disturbing factor rather than a source of vegetation's structural information. In this paper, kernel-based vegetation index (KVI) is constructed based on the semi-empirical kernel-based BRDF model parameters. Validation results of ground measured bidirectional reflection data of different vegetation types show: kernel-based vegetation index has better linear relationship with corresponding vegetation's leaf area index (LAI) than widely used normalized difference vegetation index does. The results of upscaling KVI from local scale to global large scale suggest the effect of scale needs to be considered when using it to different scale data sets. This study suggests that KVI provides a new method of better using multi-spectrum and multi-angle reflectance data, and has certain potential for multi-angular remote sensing applications