Advancing Volcanic SO2 Plume monitoring with a Zero-Shot segmentation approach using Sentinel 5P Tropomi and the SAM2 Foundation Model

Volcanic eruptions emit large quantities of sulfur dioxide (SO₂) and thermal energy, affecting atmospheric chemistry, aerosol formation, and Earth’s radiative balance. Monitoring these emissions is crucial for understanding eruption dynamics, evaluating climatic impacts, and improving early warning systems. Satellite-based Earth observation, particularly with Sentinel-5P and its TROPOspheric Monitoring Instrument (TROPOMI), offers global coverage for detecting volcanic SO₂, but existing methods, often based on thresholding, tend to lack robustness, especially when models must generalize across diverse volcanic contexts.

Here, we introduce a zero-shot scene-segmentation approach for volcanic plume recognition based on the Segment Anything Model 2 (SAM2), a vision Foundation Model (FM) pretrained on large-scale visual dataset. Without any task-specific retraining, SAM2 accurately segments volcanic SO₂ plumes in Sentinel-5P SO₂ images.  A dedicated prompting procedure is adopted to drive the object recognition process.

The method shows strong performance not only for eruptions with compact, well-isolated SO₂ plumes, such as Mount Etna and Shishaldin, but also in events where the plume disperses over several hundred kilometres, as observed for the Hunga Tonga eruption. Preliminary evaluations indicate performance competitive with, and in some cases exceeding, conventional approaches, while maintaining near-real-time processing capability and avoiding the use of large labeled datasets.    

These results demonstrate the potential of general-purpose vision foundation models for scalable, automated analysis of volcanic emissions, highlighting their relevance for operational monitoring systems and pointing toward broader applications of Foundation Models in Earth observation.