Reducing Need for Collocated Ground and Satellite based Observations in Statistical Aerosol Optical Depth Estimation

One of the biggest challenges of current climate research is to characterize and quantify the effect of aerosols on the global and local weather. This requires an accurate prediction of aerosol optical density (AOD) which is defined as the amount of loss a beam of light incurs when it passes through the atmosphere. In this paper a neural network-based data-driven prediction model is considered which uses collocated satellite (MODIS) observation and ground-based (AERONET) AOD retrievals as predictors and target respectively. This paper studies an active learning-based data collection method which will facilitate the learning of a sufficiently accurate AOD prediction model using a minimal set of labeled training data by querying the labels of only the most informative data points.