SPATIAL AVERAGING ERRORS IN CREATING HEMISPHERICAL REFLECTANCE (ALBEDO) MAPS FROM DIRECTIONAL REFLECTANCE DATA

The problems in moving from a radiance measurement made for a particul ar sun-target-sensor geometry to an accurate estimate of the hemispher ical reflectance are considerable. A knowledge-based system called VEG was used in this study to infer hemispherical reflectance. Given dire ctional reflectance(s) and the sun angle, VEG selects the most suitabl e inference technique(s) and estimates the surface hemispherical refle ctance with an estimate of the error. Ideally, VEG is applied to homog eneous vegetation. However, what is typically done in GCM (global circ ulation model) models and related studies is to obtain an average hemi spherical reflectance on a square grid cell on the order of 200 km x 2 00 km. All available directional data for a given cell are averaged (f or each view direction), and then a particular technique for inferring hemispherical reflectance is applied to this averaged data. Any given grid cell can contain several surface types that directionally scatte r radiation very differently. When averaging over a set of view angles , the resulting mean values may be atypical of the actual surface type s that occur on the ground, and the resulting inferred hemispherical r eflectance can be in error. These errors were explored by creating a s imulated scene and applying VEG to estimate the area-averaged hemisphe rical reflectance using various sampling procedures. The reduction in the hemispherical reflectance errors provided by using VEG ranged from a factor of 2-4, depending on conditions. This improvement represents a shift from the calculation of a hemispherical reflectance product o f relative value (errors of 20% or more), to a product that could be u sed quantitatively in global modeling applications, where the requirem ent is for errors to be limited to around 5-10%. The results suggest s ampling and averaging strategies for creating hemispherical reflectanc e maps for photosynthetic, carbon cycle, and climate change studies.