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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/7867

Authors: Piscini, A.*
Picchiani, M.*
Corradini, S.*
Chini, M.*
Merucci, L.*
Del Frate, F.*
Title: Retrieval of volcanic ash particle size, mass, optical depth and mass of sulfur dioxide from multispectral data using neural networks
Editors: Geological Remote Sensing Group
Issue Date: 7-Dec-2011
Keywords: Neural Network volcanic ash SO2 monitoring MODIS
Abstract: abstract In the present work, analysis techniques of satellite data in the TIR (Thermal Infrared) are shown, in the framework of volcano monitoring, in particular concerning the estimation of physical quantities related to volcanic ash clouds, ash mass, effective radius, optical thickness at 11 microns (Aerosol Optical Thickness) and the mass of sulfur dioxide, SO2, at 8.7 microns, present in the atmosphere due to volcanic eruptions. MODIS (Moderate Resolution Imaging Spectroradiometer) multispectral data is analysed, using an inversion model based on Multi Layer Perceptron Neural Networks (MLPNN). A network was built for each parameter to be retrieved. Additionally, for volcanic ash, a network for the classification of “ash image pixels” was implemented, which was then used to mask the estimates. Several network topologies were compared in terms of their performance. Concerning the training phase and testing of the networks, two MODIS images were selected covering the eruption of the Icelandic volcano Eyjafjallajokull, which took place from April to May 2010 and was one of the most disastrous natural hazards in recent years. In particular, the image acquired on May 8th 2010, at 13:20 was selected for training. The networks obtained were then applied to an image of May 9th, 2010, 12:25 UTC. The classification NNs were trained with the volcanic ash classification map obtained with the Bright Temperature Difference (BTD) algorithm, assumed to be error free. The neural networks for the quantitative estimation of the parameters associated with volcanic ash, mass, effective radius AOT and SO2, were instead trained with maps obtained using estimation algorithms based on simulated radiances at the top of the atmosphere (TOA), generated in turn applying a radiative transfer model (RTM) to remote sensing data. The networks proved very effective in solving the inversion problem related to the estimation of the parameters of the volcanic cloud, settling the crucial issue related to false alarms in the detection of volcanic ash. Furthermore, once the training phase is complete, NNs provide a faster inversion technique, useful for the applications. From this point of view the technique satisfies the need to respond quickly as a result of disastrous natural hazards, such as volcanic eruptions. In addition, the comparison between network topologies revealed that, for a given truth, a network with few inputs, but containing information on the physics, is better able to model nonlinear functional relations, proving more robust and therefore more able to generalize the phenomenon. Instead, a network ingesting all the sensor bands would probably require pruning to improve its ability to generalize. Future activities include testing the effectiveness of the technique under different lighting conditions (night images) and on other types of multispectral data, such as that provided by high temporal resolution sensors like SEVIRI-MSG, on board the METEOSAT second Generation satellites. The latter would be particularly suitable considering its exceptional quick response characteristics for real-time monitoring of the atmosphere. The use of hyperspectral data, recently used for the estimation of parameters associated with volcanic clouds, is also under consideration for future work.
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