Semi-supervised combination of experts for aerosol optical depth estimation

Aerosols are small airborne particles produced by natural and man-made sources. Aerosol Optical Depth (AOD), recognized as one of the most important quantities in understanding and predicting the Earth's climate, is estimated daily on a global scale by several Earth-observing satellite instruments. Each instrument has different coverage and sensitivity to atmospheric and surface conditions, and, as a result, the quality of AOD estimated by different instruments varies across the globe. We present a semi-supervised method for learning how to aggregate estimations from multiple satellite instruments into a more accurate estimate, where labels come from a small number of accurate and expensive ground-based instruments. The method also accounts for the problem of missing experts, an issue inherent to the AOD estimation task. By assuming a context-dependent prior, the model is capable of incorporating additional information and providing estimates even when there are no available experts. Moreover, the proposed method uses a latent variable to partition the data, so that in each partition the expert AOD estimations are aggregated in a different, optimal way. We applied the method to combine global AOD estimations from 5 instruments aboard 4 satellites, and the results indicate it can successfully exploit labeled and unlabeled data to produce accurate aggregated AOD estimations.