Characterizing land surface anisotropy from AVHRR data at a global scale using high performance computing

Abstract

We used the multi-temporal ten-day composite data from the Advanced Very High Resolution Radiometer (AVHRR) for the years 1983 to 1986 to retrieve the Bidirectional Reflectance Distribution Function (BRDF) using high performance computing techniques. Three different models are used: a simple linear model, a semi-empirical iterative model and a temporal model. The objectives of this study were to compare the performance of different BRDF models at a global scale, assess the computational requirements and optimize the algorithm implementation using high performance computational techniques, and to determine if there is any coherent spatial structure in the coefficients of different BRDF models corresponding to different land cover types. The standard error between model computed reflectances and the input data was used to quantify the performance of the models. Even though the iterative model is computationally more expensive (158 minutes) than either the simple linear model (15 minutes) or the temporal model (16 minutes), the results from all the three models were very similar when the BRDF was estimated at discrete time periods. If the BRDF models were applied without dividing the input data into discrete time intervals, then the temporal model gave better results than the other two. All the models were run on an IBM SP2 parallel machine with 16 CPUs. Most of the mountainous and snow covered areas in high latitudes had null values since the cloud screening algorithm used in the Pathfinder processing performed poorly in distinguishing between snow and clouds. The BRDF coefficients of the iterative model and the Fourier coefficients of the temporal model showed a strong spatial structure corresponding to known variations in land cover.