Reduction of ground-based sensor sites for spatio-temporal analysis of aerosols
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Aerosol optical thickness retrieval from satellite observation using support vector regression
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
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As a complement to the conventional deterministic geophysical algorithms, we consider a faster, but less accurate approach: training regression models to predict aerosol optical thickness (AOT) from radiance data. In our study, neural networks trained on a global data set are employed as a global retrieval method. Inverse distance spatial interpolation and region-specific neural networks trained on restricted, localized areas provide local models. We then develop two integrated statistical methods: local error correction of global retrievals and an optimal weighted average of global and local components. The algorithms are evaluated on the problem of deriving AOT from raw radiances observed by the Multi-angle Imaging SpectroRadiometer (MISR) instrument onboard NASA's Terra satellite. Integrated statistical approaches were clearly superior to global and local models alone. The best compromise between speed and accuracy was obtained through the weighted averaging of global neural networks and spatial interpolation. The results show that, while much faster, statistical retrievals can be quite comparable in accuracy to the far more computationally demanding deterministic methods. Differences in quality vary with season and model complexity.