A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO<sub>2</sub> using MODIS data

Abstract

Abstract. In this work neural networks (NNs) have been used for the retrieval of volcanic ash and sulfur dioxide (SO 2 ) parameters based on Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral measurements. Different neural networks were built in order for each parameter to be retrieved, for experimenting with different topologies and evaluating their performances. The neural networks' capabilities to process a large amount of new data in a very fast way have been exploited to propose a novel applicative scheme aimed at providing a complete characterization of eruptive products.As a test case, the May 2010 Eyjafjallajókull eruption has been considered. A set of seven MODIS images have been used for the training and validation phases.In order to estimate the parameters associated to the volcanic eruption, such as ash mass, effective radius, aerosol optical depth and SO 2 columnar abundance, the neural networks have been trained using the retrievals from wellknown algorithms. These are based on simulated radiances at the top of the atmosphere and are estimated by radiative transfer models.Three neural network topologies with a different number of inputs have been compared: (a) three thermal infrared MODIS channels, (b) all multispectral MODIS channels and (c) the channels selected by a pruning procedure applied to all MODIS channels.Results show that the neural network approach is able to estimate the volcanic eruption parameters very well, showing a root mean square error (RMSE) below the target data standard deviation (SD). The network built considering all the MODIS channels gives a better performance in terms of specialization, mainly on images close in time to the training ones, while the networks with less inputs reveal a better generalization performance when applied to independent data sets. In order to increase the network's generalization capability and to select the most significant MODIS channels, a pruning algorithm has been implemented. The pruning outcomes revealed that channel sensitive to ash parameters correspond to the thermal infrared, visible and mid-infrared spectral ranges.The neural network approach has been proven to be effective when addressing the inversion problem for the estimation of volcanic ash and SO 2 cloud parameters, providing fast and reliable retrievals, important requirements during volcanic crises.

Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System

“…This approach is easily applicable, but also prone to generate false alarms. A number of methods have been developed to overcome this issue [20], including 3-band algorithms [48], the BTD algorithm with water vapor correction (BTD-WVC), the Robust Satellite Technique (RST) specifically configured for volcanic ash, and shallow neural networks [89,90]. Future developments should therefore implement automated S3 processing to monitor volcanic ash propagation in the atmosphere, which poses a major threat for air traffic in particular.…”

Automatic volcanic ash detection from MODIS observations using a back-propagation neural network

“…2 does not contain the bright red/pink signature usually observed for ash, although a 15 km plume is reported (GVP) and an ash signature is clear in the visible image. When high amounts of water are incorporated into ash clouds, they lose their ash signature and begin to appear more like meteorological clouds than ash clouds (Prata, 2009), which explains why the neural network is not discerning the volcanic cloud in this particular case. There is also no SO 2 -rich ash detected because a SO 2 signature is not present with this eruption at this time.…”

“…Because MODIS channels 29, 31, and 32 cover this spectral range, MODIS images were obtained and band combinations were applied to determine areas of ash and SO 2 in each eruptive column. Sulfur dioxide is very absorptive in 7.3 and 8.6 µm channels (Prata et al, 2007). However, the 7.3 µm channel is not as sensitive to the total SO 2 column as the 8.6 µm channel because the 7.3 µm channel lies within a band that is also sensitive to water vapor (Sears et al, 2013) (see Fig.…”

“…Previous research determining locations and loadings of SO 2 and ash has used one or a combination of the following satellite instruments: Total Ozone Mapping Spectrometer (TOMS), Moderate Resolution Imaging Spectrometer (MODIS), Atmospheric Infrared Sounder (AIRS), Spinning Enhanced Visible and Infrared Imager (SEVIRI), Ozone Monitoring Instrument (OMI), and the Advanced Very High Resolution Radiometer (AVHRR) (Ackerman, 1997;Carn et al, 2009a;Carn and Prata, 2010;Feltz et al, 2006;Prata et al, 2007Prata et al, , 2010Theys et al, 2013;Pavolonis et al, 2006). Most of these methods are either physical inversions or statistically derived threshold methods.…”

A tailored multi-model ensemble for air traffic management: Demonstration and evaluation for the Eyjafjallajökull eruption in May 2010

“…Like other spaceborne retrievals of volcanic ash (e.g. Prata, 1989a;Prata and Grant, 2001;Francis et al, 2012;Prata and Prata, 2012;Pavolonis et al, 2013;Pugnaghi et al, 2013;Piscini et al, 2014) it exploits the spectral signatures of volcanic ash in the atmosphere, in particular the reverse absorption effect between two window channels centred at 10.8 and 12.0 µm (Prata, 1989a, b) and the information coming from all thermal SEVIRI channels. Thus, it is applicable during day and night.…”