TITLE:
A Neural Network Algorithm to Detect Sulphur Dioxide Using IASI Measurements
AUTHORS:
Alessandro Piscini, Elisa Carboni, Fabio Del Frate, Roy Gordon Grainger
KEYWORDS:
Artificial Neural Networks, Pattern Recognition, Remote Sensing of Volcanoes, Volcano Monitoring, Hyperspectral, Volcanic Sulphur Dioxide
JOURNAL NAME:
Advances in Remote Sensing,
Vol.3 No.4,
December
4,
2014
ABSTRACT: The remote sensing of
volcanic sulphur dioxide (SO2) is important because it is used as a
proxy for volcanic ash, which is dangerous to aviation and is generally more
difficult to discriminate. This paper presents an Artificial Neural Network
(ANN) algorithm that recognizes volcanic SO2 in the atmosphere using hyperspectral
remotely sensed data from the IASI instrument aboard the Metop-A satellite. The
importance of this approach lies in exploiting all thermal infrared spectral
information of IASI and its application to near real-time volcanic monitoring
in a fast manner. In this paper, the ANN algorithm is demonstrated on data of
the Eyjafjallajokull volcanic eruption (Iceland) during the months of April and
May 2010, and on the Grímsvotn eruption occurring during May 2011. The
algorithm consists of a two output neural network classifier trained with a
time series consisting of some hyperspectral eruption datasets collected during
14 April to 14 May 2010 and a few from 22 to 26 May 2011. The inputs were all
channels (441) in the IASI v3 band and the target outputs (truth)
were the corresponding retrievals of SO2 amount obtained with an optimal
estimation method. The validation results for the Eyjafjallajokull independent
data-sets had an overall accuracy of 100% and no commission errors, therefore
demonstrating the feasibility of estimating the presence of SO2 using a neural network approach also a
in cloudy sky conditions. Although the validation of the neural network
classifier on datasets from the Grímsvotn eruption had no commission errors,
the overall accuracies were lower due to the presence of omission errors.
Statistical analysis revealed that those false negatives lie near the detection
threshold for discriminating pixels affected by SO2. This
demonstrated that the accuracy in classification is strictly related to the
sensitivity of the model. The lower accuracy obtained in detecting SO2 for Grímsvotn validation dates might
also be caused by less statistical knowledge of such an eruption during the
training phase.