Neural Network–Based Detection of the Etna Volcanic Cloud: From MSG-SEVIRI to MTG-FCI

The detection and monitoring of volcanic clouds are critical for hazard assessment and aviation safety. In this study, we present the application of a neural network (NN) model trained on Spinning Enhanced Visible and Infrared Imager (SEVIRI) data to detect the volcanic cloud produced during the Mount Etna eruption of 27 December 2025. The analysis focuses on evaluating the model’s ability to generalize across satellite instruments by extending its application to data acquired by the Flexible Combined Imager (FCI) onboard the Meteosat Third Generation (MTG) platform. Furthermore, a volcanic cloud quantitative analysis was conducted by applying the Volcanic Plume Removal (VPR) algorithm, using the neural network–based volcanic cloud detection as input.

The primary objective of this work is to demonstrate the cross-instrument applicability of the neural network model, highlighting its robustness and adaptability to next-generation geostationary sensors. The results show that the model effectively identifies volcanic cloud structures in both SEVIRI and FCI observations, emphasizing the potential of artificial intelligence techniques for reliable volcanic cloud detection.

The second objective of this study is to present the first volcanic cloud quantitative analysis using FCI data and to compare the results with those derived from SEVIRI observations. The results demonstrate a higher sensitivity of FCI compared to SEVIRI, which can be attributed to the advanced sensor technology and the improved spatial resolution of the instrument.

This approach represents a significant step toward the development of a near-real-time monitoring system, enabling automated detection and subsequent quantification of volcanic clouds. Such a system has significant implications for operational volcano monitoring and hazard mitigation, enabling the timely and consistent delivery of information during eruptive events.