AI-driven volcanic SO2 estimates using MSG-SEVIRI and Sentinel-5P TROPOMI

Sulfur dioxide (SO2) is, after H2O and CO2, the most abundant volcanic gas released during shallow magma degassing and is readily detectable and quantifiable by remote sensing. Monitoring SO2 provides key information on magma ascent rates, conduit dynamics, and eruption style and intensity, thereby supporting volcano monitoring and hazard assessment. The TROPOMI instrument onboard the low Earth Orbit Sentinel-5 Precursor delivers highly accurate and sensitive SO2 retrievals but with limited temporal coverage (typically one overpass per day). In contrast, SEVIRI, onboard the geostationary MSG satellite, offers continuous high-frequency observations every 5–15 min, enabling real-time tracking of plume evolution. To further improve SO2 detection capabilities, this study combines TROPOMI products with data from the MSG‑SEVIRI radiometer, by transferring TROPOMI’s retrieval accuracy to SEVIRI’s high-temporal-resolution observations. To this purpose, a data-driven AI model was implemented to estimate SO2 vertical column densities (VCDs) at SEVIRI spatial and temporal resolution, using TROPOMI observations as reference. In this study, SO2 VCDs were retrieved from Sentinel-5P TROPOMI Level 2 Offline DOAS measurements using slant column densities, averaging kernels, and plume height-adapted air mass factors. A multilayer perceptron was designed with an input layer receiving all available spectral bands from SEVIRI and a single linear output neuron, corresponding to the SO2 VCD measured by TROPOMI. This approach enables SEVIRI data to inherit the sensitivity of TROPOMI while preserving SEVIRI’s native high-temporal-resolution and dense spatial sampling. Mount Etna (Italy), a persistently degassing open-conduit volcano, was selected as a test case. The trained network also allowed SO2 retrieval from SEVIRI imagery acquired before Sentinel-5P’s launch, enabling the reconstruction of long-term degassing trends. SO2 fluxes were computed and quantitatively validated against ground-based monitoring data. This integrated technique provides an effective tool for rapid and reliable volcanic hazard assessment, improving current retrieval methods and enhancing early-warning capability for aviation safety and climate studies.