Minimizing Reflectance Anisotropy Effects in Airborne Spectroscopy Data Using Ross–Li Model Inversion With Continuous Field Land Cover Stratification

The spectral and radiometric quality of airborne imaging spectrometer data is affected by the anisotropic reflectance behavior of the imaged surface. Illumination and observation angle-dependent patterns of surface reflected radiation propagate into products, hinder quantitative assessment of biophysical/biochemical parameters, and decrease the comparability of data from multiple flight lines. The Ross-Li model, originally developed for multiangular observations, can be inverted to estimate and correct for surface anisotropy effects. This requires land cover be stratified into distinct types of scattering behavior. When the observations subsumed in these classes cover a range of view angles, a pseudo multiangular view on the surface can be employed to invert the Ross-Li model. A discrete land cover classification, however, bears the risk of inappropriate scattering correction resulting in spatial artifacts in the corrected data, predominantly in transition regions of two land cover types (e.g., soil and sparse vegetation with varying fractions). We invert the Ross-Li model on continuous land cover fraction layers. We decompose land cover in dominating structural types using linear spectral unmixing. Ross-Li kernel weights and formulations are estimated for each type independently; the correction is then applied pixel-wise according to the fractional distribution. The corrected Airborne Prism EXperiment imaging spectrometer data show significant reduction of anisotropic reflectance effects of up to 90% (average 60% to 75%, $p=0.05$), measured in the overlapping regions of adjacent flight lines. No spatial artifacts or spectral irregularities are observed after correction.