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

We used the multi-temporal ten-day composite data from the Advanced Very H igh Resolution Radiometer (AVH RR) for the years 1983 to 1986 to retrieve the Bidirectional Re ectance D istribution Function (BRD F ) using high performance computing techniques. Three diVerent 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 diVerent BRD F 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 coeYcients of diVerent BRD F models corresponding to diVerent land cover types. The standard error between model computed re ectances 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 BRD F was estimated at discrete time periods. If the BRD F 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 SP 2 parallel machine with 16 CP U s. M ost of the mountainous and snow covered areas in high latitudes had null values since the cloud screening algorithm used in the P ath nder processing performed poorly in distinguishing between snow and clouds. The BRD F coeYcients of the iterative model and the Fourier coeYcients of the temporal model showed a strong spatial structure corresponding to known variations in land cover.