AI-Based Multi-Sensor Data Fusion for Near Real-Time Monitoring of Effusive Volcanic Activity: A Case Study of Mount Etna (November 2022 – February 2023) for thermal activity volcano monitoring

Article Content

Abstract

Earth Observations (EO) play a crucial role in global monitoring of volcanic activity worldwide. Satellite data are continuously acquired by multispectralsensors on board of polar and geostationary satellites orbiting the Earth. EO allows to track ongoing volcanic activity by retrieving eruptive parameters such as the amount of lava erupted in near real time. The heterogeneity of satellite sensors in terms
of spatial and temporal resolution requires advanced techniques to automatically estimate volcanic features and combine their outcomes. Here, the potential of integrating Artificial Intelligence (AI) techniques has been demonstrated in processing large amounts of heterogeneous satellite data used to monitor the effusive activity in near real time at Mount Etna from November 2022 to February
2023. Data provided by satellite sensors, including the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI), enable reliable estimates of Volcanic Radiative Power (VRP),
areal coverage of lava flows, Time Averaged Discharge Rate (TADR) and the lava volume erupted, and areal coverage of lava flows thanks to Sentinel-2 MSI.

Issue

Vol. 68 No. 2 (2025)

Section

SPECIAL ISSUE: Artificial intelligence for Volcanology

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How to Cite

Di Bella, G. S. (2025). AI-Based Multi-Sensor Data Fusion for Near Real-Time Monitoring of Effusive Volcanic Activity: A Case Study of Mount Etna (November 2022 – February 2023) for thermal activity volcano monitoring. Annals of Geophysics, 68(2), V221. https://doi.org/10.4401/ag-9185