Cloud-Based Workflow for Detection and Flux Estimation of Volcanic SO₂ Plumes Using Sentinel-5P/TROPOMI and ERA5 Data

Monitoring volcanic sulfur dioxide (SO₂) is a key element for understanding eruptive processes and assessing their atmospheric and climatic impacts. SO₂ emissions are among the primary indicators of magmatic activity and play a fundamental role both in local-scale risk management, including air quality and aviation safety, and in regional to global climate processes through the formation of sulfate aerosols capable of altering the Earth's radiative balance.

In recent years, satellite observations and the cloud computing geospatial platform have revolutionised the monitoring of volcanic emissions. In particular, the Sentinel-5P satellite, part of the Copernicus programme, carries the TROPOspheric Monitoring Instrument (TROPOMI) spectrometer, designed for high-resolution monitoring of atmospheric trace gases.

In this context, cloud-based geospatial platforms such as Google Earth Engine (GEE) provide an efficient computational environment for large-scale processing of satellite datasets. GEE enables scalable implementation of raster classification, morphological operations, and vector extraction workflows without the need for local high-performance computing resources. Here, we present a reproducible workflow for the detection, segmentation, and spatial characterization of volcanic SO2 flux using TROPOMI satellite observations and ERA5 data. The method integrates machine learning classification with connected component analysis to isolate individual volcanic plume structures. Each detected plume is then characterised using geometric centroids and distance metrics relative to the volcanic vent, allowing for quantitative assessment of plume spatial distribution and dispersion patterns. ERA-5 data are then integrated to compute SO2 flux. The approach is demonstrated through a case study of Mount Etna (Italy), one of the most active volcanoes in the world and a persistent source of SO2 emissions. The results highlight the capability of combining satellite remote sensing and cloud-based geospatial analysis for improved characterization of volcanic gas emissions and plume dynamics.