Correction of reflectance anisotropy: a multi-sensor approach

Abstract

Quantitative mapping by means of hyperspectral remote sensing (HRS) can be hampered by reflectance anisotropy emerging in large field of view (FOV) optics, and may contain spectral radiometric distortions. This paper presents an algorithm for the rectification of reflectance anisotropy for rough terrain. A new method is offered for correction of radiometric bias caused by topography and sensing geometry. The correction of HRS data of lawn grass is demonstrated, and the method is tested on a large park area. To record elevation we used airborne laser scanning data to obtain a digital surface model (DSM). The Compact Airborne Spectral Imager (CASI) recorded reflectance of the same area. Anisotropy of reflectance was recorded by a laboratory spectro-goniometer. An analysis of the effect of correction on the normalized difference vegetation index (NDVI) shows that even moderate slopes, medium sensor FOV and high illumination conditions will result in reflectance anisotropy. Further analysis shows a clear inverse relationship between sensitivity of interpretation and spatial or spectral resolutions. We conclude with an outlook on the utilization of this method among other pre-processing tasks.