Most Land cover types are ``anisotropic'', that is, the solar radiation reflected by the surface is not uniform in all directions. Characterizing the Bidirectional Reflectance Distribution Function (BRDF) of the earth's surface is critical in understanding surface anisotropy. Though there are several methods to retrieve the BRDF of various land cover types, most of them have been applied over small data sets collected either on ground or from aircraft at limited spatial and temporal scales.

In this paper, we use multi-angular, multi-temporal and multi-band Pathfinder AVHRR Land (PAL) data set to retrieve the global BRDF in the red and near infrared wavelengths. The PAL data set used in our study has a spatial resolution of 8-km and 10-day composite data for four years (1983 to 1986). In particular, we develop high performance algorithms to retrieve global BRDF using three widely different models. Given the volume of data involved (about 27 GBytes), we attempt to optimize the I/O time as well as minimize the overall computational complexity. Our algorithms access the global data once, followed by a redistribution of land pixel data to balance the computational loads among the different nodes of a multiprocessor system. This strategy results in an optimized
I/O access time with efficiently balanced computations across the nodes. Experimental data on a 16-node IBM SP2 is used to support these claims and to illustrate the scalability of our algorithms. (Also cross-referenced as UMIACS-TR-97-55)