A New Algorithm for the Global-Scale Quantification of Volcanic SO2 Exploiting the Sentinel-5P TROPOMI and Google Earth Engine

Sulfur dioxide (SO2) is sourced by degassing magma in the shallow crust; hence its monitoring provides information on the rates of magma ascent in the feeding conduit and the style and intensity of eruption, ultimately contributing to volcano monitoring and hazard assessment. Here, we present a new algorithm to extract SO2 data from the TROPOMI imaging spectrometer aboard the Sentinel-5 Precursor satellite, which delivers atmospheric column measurements of sulfur dioxide and other gases with an unprecedented spatial resolution and daily revisit time. Specifically, we automatically extract the volcanic clouds by introducing a two-step approach. Firstly, we used the Simple Non-Iterative Clustering segmentation method, which is an object-based image analysis approach; secondly, the K-means unsupervised machine learning technique is applied to the segmented images, allowing a further and better clustering to distinguish the SO2. We implemented this algorithm in the open-source Google Earth Engine computing platform, which provides TROPOMI imagery collection adjusted in terms of quality parameters. As case studies, we chose three volcanoes: Mount Etna (Italy), Taal (Philippines) and Sangay (Ecuador); we calculated sulfur dioxide mass values from 2018 to date, focusing on a few paroxysmal events. Our results are compared with data available in the literature and with Level 2 TROPOMI imagery, where a mask is provided to identify SO2, finding an optimal agreement. This work paves the way to the release of SO2 flux time series with reduced delay and improved calculation time, hence contributing to a rapid response to volcanic unrest/eruption at volcanoes worldwide.