Aerosol Optical Depth Retrieval by Neural Networks Ensemble with Adaptive Cost Function

Aerosol Optical Depth (AOD) indicates the amount of depletion that a beam of radiation undergoes as it passes through the atmosphere. In this study a datadriven approach based on training neural networks for AOD prediction was considered. To train the predictor, we used more than a thousand collocated data points whose attributes were derived from MODIS instrument satellite observations and whose target AOD variable was obtained from the groundbased AERONET instruments. In order to minimize the relative error, which is performance measure preferred by domain scientists, we trained an ensemble of neural networks with adaptive cost functions. AOD prediction accuracy of neural networks was compared to the recently developed operational MODIS Collection 005 retrieval algorithm. Results obtained over the entire globe during the first six months of year 2005 showed that neural networks were more accurate than the operational retrieval algorithm.