The presented dataset provides a consistent time series of SO₂ total mass (expressed in kilotons) from Mount Etna (Sicily, Italy), covering the period from 2018 to the present. The data have been obtained from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which has been operational since 2018, delivering daily atmospheric column measurements of sulfur dioxide and other gases at unprecedented spatial resolution. Volcanic SO₂ clouds were automatically identified through a two-step procedure: first, the Simple Non-Iterative Clustering (SNIC) segmentation method was applied, an object-based image analysis technique; second, K-means unsupervised machine learning clustering was used on the segmented imagery to further improve cloud discrimination. The algorithm has been implemented in the open-access Google Earth Engine platform, enabling efficient processing of the TROPOMI imagery collection while incorporating quality control filters. This methodological framework supports the generation of SO₂ total mass time series with minimal delay and optimized computation time, providing a valuable tool for rapid and reliable monitoring of volcanic emissions and for enhancing volcanic hazard assessment capabilities.For further details please refer to the paper by Dozzo et al., 2025 (Dozzo, M., Aiuppa, A., Bilotta, G., Cappello, A., & Ganci, G. (2025). A New Algorithm for the Global-Scale Quantification of Volcanic SO2 Exploiting the Sentinel-5P TROPOMI and Google Earth Engine. Remote Sensing, 17(3), 534. https://doi.org/10.3390/rs17030534).
