Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

• Published in: Atmospheric Measurement Techniques
• Main Authors: Petracca, Ilaria, De Santis, Davide, Picchiani, Matteo, Corradini, Stefano, Guerrieri, Lorenzo, Prata, Fred, Merucci, Luca, Stelitano, Dario, Del Frate, Fabio, Salvucci, Giorgia, Schiavon, Giovanni
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: article; Verlagsveröffentlichung; Eyjafjallajökull
• Online Access: https://doi.org/10.5194/amt-15-7195-2022; https://noa.gwlb.de/receive/cop_mods_00063972; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062883/amt-15-7195-2022.pdf; https://amt.copernicus.org/articles/15/7195/2022/amt-15-7195-2022.pdf

Accurate automatic volcanic cloud detection by means of satellite data is a challenging task and is of great concern for both the scientific community and aviation stakeholders due to well-known issues generated by strong eruption events in relation to aviation safety and health impacts. In this context, machine learning techniques applied to satellite data acquired from recent spaceborne sensors have shown promising results in the last few years. This work focuses on the application of a neural-network-based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. A classification of meteorological clouds and of other surfaces comprising the scene is also carried out. The neural network has been trained with MODIS (Moderate Resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallajökull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the comparable latitudes of the eruptions permit an extension of the approach to SLSTR, thereby overcoming the lack in Sentinel-3 products collected in previous mid- to high-latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared to RGB visual inspection and BTD (brightness temperature difference) procedures. Moreover, the comparison between the ash cloud obtained by the neural network (NN) and a plume mask manually generated for the specific SLSTR images considered shows significant agreement, with an F-measure of around 0.7. Thus, the proposed approach allows for an automatic image classification during eruption events, and it is also considerably faster than time-consuming manual algorithms. Furthermore, the whole image classification indicates the overall reliability of the algorithm, particularly for recognition and discrimination between volcanic clouds and other objects.