Correction of Reflectance Anisotropy Effects of Vegetation on Airborne Spectroscopy Data and Derived Products

Directional effects in airborne imaging spectrometer (IS) data are mainly caused by anisotropic reflectance behavior of surfaces, commonly described by bi-directional reflectance distribution functions (BRDF). The radiometric and spectral accuracy of IS data is known to be highly influenced by such effects, which prevents consistent comparison of products. Several models were developed to approximate surface reflectance anisotropy for multi-angular observations. Few studies were carried out using such models for airborne flight lines where only a single observation is available for each ground location. In the present work, we quantified and corrected reflectance anisotropy on a single airborne HyMap flight line using a Ross-Li model. We stratified the surface in two vegetation structural types (different in vertical structuring) using spectral angle mapping, to generate a structure dependent set of angular observations. We then derived a suite of products [indices (structure insensitive pigment index, normalized difference vegetation index, simple ratio index, and anthocyanin reflectance index) and inversion-based (SAIL/PROSPECT-leaf area index, Cw, Cdm, Cab)] from corrected and uncorrected images. Non-parametric analysis of variance (Kruskal-Wallis test) showed throughout significant improvements in products from corrected images. Data correction resulting in airborne nadir BRDF adjusted reflectance (aNBAR) showed uncertainty reductions from 60 to 100% (p-value = 0.05) as compared to uncorrected and nadir observations. Using sparse IS data acquisitions, the use of fully parametrized BRDF models is limited. Our normalization scheme is straightforward and can be applied with illumination and observation geometry being the only a priori information. We recommend aNBAR generation to precede any higher level airborne IS product generation based on reflectance data.