Articles | Volume 14, issue 5
https://doi.org/10.5194/amt-14-3673-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-3673-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 May 2021
Research article |
| 20 May 2021
| 20 May 2021
Volcanic SO2 effective layer height retrieval for the Ozone Monitoring Instrument (OMI) using a machine-learning approach
Nikita M. Fedkin
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Nickolay A. Krotkov
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Pascal Hedelt
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF),
Oberpfaffenhofen, Germany
Diego G. Loyola
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF),
Oberpfaffenhofen, Germany
Russell R. Dickerson
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD, USA
Robert Spurr
RT Solutions Inc., Cambridge, MA, USA
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Cited
14 citations as recorded by crossref.
- Volcanic SO2 layer height by TROPOMI/S5P: evaluation against IASI/MetOp and CALIOP/CALIPSO observations M. Koukouli et al. https://doi.org/10.5194/acp-22-5665-2022
- Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review R. Mota et al. https://doi.org/10.3390/rs16101789
- Ultraviolet and visible remote sensing of volcanic gas emissions C. Kern https://doi.org/10.1016/j.jvolgeores.2025.108423
- Improved retrieval of SO2 plume height from TROPOMI using an iterative Covariance-Based Retrieval Algorithm N. Theys et al. https://doi.org/10.5194/amt-15-4801-2022
- Robust Satellite Techniques (RSTs) for SO2 Detection with MSG-SEVIRI Data: A Case Study of the 2021 Tajogaite Eruption R. Mota et al. https://doi.org/10.3390/rs17193345
- A new machine-learning-based analysis for improving satellite-retrieved atmospheric composition data: OMI SO2 as an example C. Li et al. https://doi.org/10.5194/amt-15-5497-2022
- Enhanced characterization of SO2 plume height and column density using the second UV spectral band of TROPOMI L. Fabris et al. https://doi.org/10.5194/amt-19-1801-2026
- Artificial Intelligence in Aviation Safety: Systematic Review and Biometric Analysis G. Demir et al. https://doi.org/10.1007/s44196-024-00671-w
- Monitoring Earth's atmosphere with Sentinel-5 TROPOMI and Artificial Intelligence: Quantifying volcanic SO2 emissions C. Corradino et al. https://doi.org/10.1016/j.rse.2024.114463
- APPLICATION OF MACHINE LEARNING IN RADIATIVE HEAT TRANSFER W. Chen & T. Ren https://doi.org/10.1615/AnnualRevHeatTransfer.2025059506
- Volcanic plume height during the 2021 Tajogaite eruption (La Palma) from two complementary monitoring methods – implications for satellite-based products Á. Barreto et al. https://doi.org/10.5194/amt-19-1385-2026
- Evaluating the assimilation of S5P/TROPOMI near real-time SO2 columns and layer height data into the CAMS integrated forecasting system (CY47R1), based on a case study of the 2019 Raikoke eruption A. Inness et al. https://doi.org/10.5194/gmd-15-971-2022
- Volcanic Activity Classification Through Semi-Supervised Learning Applied to Satellite Radiance Time Series F. Spina et al. https://doi.org/10.3390/rs17101679
- A 19-year record of atmospheric sulphur dioxide (SO2) derived from IASI measurements L. Clarisse et al. https://doi.org/10.5194/essd-18-6667-2026
14 citations as recorded by crossref.
- Volcanic SO2 layer height by TROPOMI/S5P: evaluation against IASI/MetOp and CALIOP/CALIPSO observations M. Koukouli et al. https://doi.org/10.5194/acp-22-5665-2022
- Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review R. Mota et al. https://doi.org/10.3390/rs16101789
- Ultraviolet and visible remote sensing of volcanic gas emissions C. Kern https://doi.org/10.1016/j.jvolgeores.2025.108423
- Improved retrieval of SO2 plume height from TROPOMI using an iterative Covariance-Based Retrieval Algorithm N. Theys et al. https://doi.org/10.5194/amt-15-4801-2022
- Robust Satellite Techniques (RSTs) for SO2 Detection with MSG-SEVIRI Data: A Case Study of the 2021 Tajogaite Eruption R. Mota et al. https://doi.org/10.3390/rs17193345
- A new machine-learning-based analysis for improving satellite-retrieved atmospheric composition data: OMI SO2 as an example C. Li et al. https://doi.org/10.5194/amt-15-5497-2022
- Enhanced characterization of SO2 plume height and column density using the second UV spectral band of TROPOMI L. Fabris et al. https://doi.org/10.5194/amt-19-1801-2026
- Artificial Intelligence in Aviation Safety: Systematic Review and Biometric Analysis G. Demir et al. https://doi.org/10.1007/s44196-024-00671-w
- Monitoring Earth's atmosphere with Sentinel-5 TROPOMI and Artificial Intelligence: Quantifying volcanic SO2 emissions C. Corradino et al. https://doi.org/10.1016/j.rse.2024.114463
- APPLICATION OF MACHINE LEARNING IN RADIATIVE HEAT TRANSFER W. Chen & T. Ren https://doi.org/10.1615/AnnualRevHeatTransfer.2025059506
- Volcanic plume height during the 2021 Tajogaite eruption (La Palma) from two complementary monitoring methods – implications for satellite-based products Á. Barreto et al. https://doi.org/10.5194/amt-19-1385-2026
- Evaluating the assimilation of S5P/TROPOMI near real-time SO2 columns and layer height data into the CAMS integrated forecasting system (CY47R1), based on a case study of the 2019 Raikoke eruption A. Inness et al. https://doi.org/10.5194/gmd-15-971-2022
- Volcanic Activity Classification Through Semi-Supervised Learning Applied to Satellite Radiance Time Series F. Spina et al. https://doi.org/10.3390/rs17101679
- A 19-year record of atmospheric sulphur dioxide (SO2) derived from IASI measurements L. Clarisse et al. https://doi.org/10.5194/essd-18-6667-2026
Saved (final revised paper)
Latest update: 15 Sep 2026
Short summary
This study presents a new volcanic sulfur dioxide (SO2) layer height retrieval algorithm for the Ozone Monitoring Instrument (OMI). We generated a large spectral dataset with a radiative transfer model and used it to train neural networks to predict SO2 height from OMI radiance data. The algorithm is fast and takes less than 10 min for a single orbit. Retrievals were tested on four eruption cases, and results had reasonable agreement (within 2 km) with other retrievals and previous studies.
This study presents a new volcanic sulfur dioxide (SO2) layer height retrieval algorithm for the...
