Automatic retrieval of volcanic SO2 emission source from TROPOMI products

Abstract

Volcanic sulfur dioxide (SO2) satellite observations are key for monitoring volcanic activity, and for mitigation of the associated risks on both human health and aviation safety. Automatic analysis of this data source, including robust source emission retrieval, is in turn essential for near real-time monitoring applications. We have developed fast and accurate SO2 plume classifier and segmentation algorithms using classic clustering, segmentation and image processing techniques. These algorithms, applied to measurements from the TROPOMI instrument onboard the Sentinel-5 Precursor platform, can help in the accurate source estimation of volcanic SO2 plumes originating from various volcanoes. In this paper, we demonstrate the ability of different pixel classification methodologies to retrieve SO2 source emission with a good accuracy. We compare the algorithms, their strengths and shortcomings, and present plume classification results for various active volcanoes throughout the year 2021, including examples from Etna (Italy), Sangay and Reventador (Ecuador), Sabancaya and Ubinas (Peru), Scheveluch and Klyuchevskoy (Russia), as well as Ibu and Dukono (Indonesia). The developed algorithms, shared as open-source code, contribute to improving analysis and monitoring of volcanic emissions from space.

Automatic Estimation of Daily Volcanic Sulfur Dioxide Gas Flux From TROPOMI Satellite Observations: Application to Etna and Piton de la Fournaise

“…"Inverse modeling" attempts to match the observed spatial distribution of vertical column densities against simulations from a numerical (chemistry-)transport model, initialized with a weather model, thereby incorporating potentially complex atmospheric processes such as diffusion, deposition and/or chemical conversion (Behera et al, 2023;Boichu et al, 2013Boichu et al, , 2014Boichu et al, , 2015Cai et al, 2022;Eckhardt et al, 2008;Flemming & Inness, 2013;Heng et al, 2016;Kristiansen et al, 2010;Moxnes et al, 2014;Theys et al, 2013;Vira et al, 2017). 5. the "Back-trajectory" approach estimates the time-of-flight of gas parcels associated with each pixel in a satellite image, and deduces time and altitude of emissions by back-projecting these individual parcels into the emission parameter space, using a back-trajectory model (Esse et al, 2024;Hayer et al, 2023;Hughes et al, 2012;Markus et al, 2023;Queißer et al, 2019).…”

“…This is achieved by outlining the plume boundary or by removing soundings with a low column amount, which introduces a bias whose impact is seldom quantified. Finally, none of the aforementioned methods is distributed as open‐source code (except for the source separation algorithm of Markus et al., 2023) and none is associated with a publicly accessible web application.…”

“…the “Back‐trajectory” approach estimates the time‐of‐flight of gas parcels associated with each pixel in a satellite image, and deduces time and altitude of emissions by back‐projecting these individual parcels into the emission parameter space, using a back‐trajectory model (Esse et al., 2024; Hayer et al., 2023; Hughes et al., 2012; Markus et al., 2023; Queißer et al., 2019).…”

Automatic estimation of daily volcanic sulfur dioxide gas flux from TROPOMI satellite observations: application to Etna and Piton de la Fournaise

“…"Inverse modelling" attempts to match the observed spatial distribution of vertical column densities against simulations from a numerical (chemistry-)transport model, initialized with a weather model, thereby incorporating potentially complex atmospheric processes such as diffusion, deposition and/or chemical conversion (Eckhardt et al, 2008;Kristiansen et al, 2010;Boichu et al, 2013;Theys et al, 2013;Flemming & Inness, 2013;Moxnes et al, 2014;Boichu et al, 2014Boichu et al, , 2015Vira et al, 2017;Heng et al, 2016;Cai et al, 2021;Behera et al, 2023). 5. the "Back-trajectory" approach estimates the time-of-flight of gas parcels associated with each pixel in a satellite image, and deduces time and altitude of emissions by back-projecting these individual parcels into the emission parameter space, using a back-trajectory model (Hughes et al, 2012;Queißer et al, 2019;Hayer et al, 2023;Markus et al, 2023;Esse et al, 2024).…”

“…This is achieved by outlining the plume boundary or by removing soundings with a low column amount, which introduces a bias whose impact is seldom quantified. Finally, none of the aforementioned methods is distributed as open-source code (except for the source separation algorithm of Markus et al, 2023) and none is associated with a publicly accessible web application.…”