A neural network approach for simultaneous retrieval of volcanic SO2 and plume height using hyperspectral measurements

In this study two neural networks were implemented in order to emulate a retrieval model and to estimate the sulphur dioxide (SO 2 ) columnar content and plume height from volcanic eruption. ANNs were trained using all IASI channels in TIR as inputs, and the corresponding values of SO 2 content and height of plume obtained using the Oxford SO 2 retrievals as target outputs. The retrieval is demonstrated for the eruption of the Eyjafjallajokull volcano (Iceland) occured in 2010 and to three IASI images of the Grimsvotn volcanic eruption that occurred in May 2011, in order to evaluate the networks for a different eruption. The results of validation, both for Eyjafjallajokull and Grimsvotn independent datasets, provided RMSE values between neural network outputs and targets lower than 20 DU for SO 2 total column and 200 mb for plume height, therefore demonstrating the feasibility to estimate SO 2 values using a neural network approach, and its importance in near real time monitoring activities, owing to its fast application. Concerning the validation carried out with neural networks on images from the Grimsvotn eruption, the RMSE of the outputs remained lower than the Standard Deviation (STD) of targets, and the neural network underestimated retrieval only where target outputs showed different statistics than those used during the training phase.