Bulletin of Volcanology

, 79:80 | Cite as

Validating the accuracy of SO2 gas retrievals in the thermal infrared (8–14 μm)

  • Andrea Gabrieli
  • John N. Porter
  • Robert Wright
  • Paul G. Lucey
Research Article
First Online:
Received:
Accepted:

Abstract

Quantifying sulfur dioxide (SO2) in volcanic plumes is important for eruption predictions and public health. Ground-based remote sensing of spectral radiance of plumes contains information on the path-concentration of SO2. However, reliable inversion algorithms are needed to convert plume spectral radiance measurements into SO2 path-concentrations. Various techniques have been used for this purpose. Recent approaches have employed thermal infrared (TIR) imaging between 8 μm and 14 μm to provide two-dimensional mapping of plume SO2 path-concentration, using what might be described as “dual-view” techniques. In this case, the radiance (or its surrogate brightness temperature) is computed for portions of the image that correspond to the plume and compared with spectral radiance obtained for adjacent regions of the image that do not (i.e., “clear sky”). In this way, the contribution that the plume makes to the measured radiance can be isolated from the background atmospheric contribution, this residual signal being converted to an estimate of gas path-concentration via radiative transfer modeling. These dual-view approaches suffer from several issues, mainly the assumption of clear sky background conditions. At this time, the various inversion algorithms remain poorly validated. This paper makes two contributions. Firstly, it validates the aforementioned dual-view approaches, using hyperspectral TIR imaging data. Secondly, it introduces a new method to derive SO2 path-concentrations, which allows for single point SO2 path-concentration retrievals, suitable for hyperspectral imaging with clear or cloudy background conditions. The SO2 amenable lookup table algorithm (SO2–ALTA) uses the MODTRAN5 radiative transfer model to compute radiance for a variety (millions) of plume and atmospheric conditions. Rather than searching this lookup table to find the best fit for each measured spectrum, the lookup table was used to train a partial least square regression (PLSR) model. The coefficients of this model are used to invert measured radiance spectra to path-concentration on a pixel-by-pixel basis. In order to validate the algorithms, TIR hyperspectral measurements were carried out by measuring sky radiance when looking through gas cells filled with known amounts of SO2. SO2–ALTA was also tested on retrieving SO2 path-concentrations from the Kīlauea volcano, Hawai’i. For cloud-free conditions, all three techniques worked well. In cases where background clouds were present, then only SO2–ALTA was found to provide good results, but only under low atmospheric water vapor column amounts.

Keywords

Inversion algorithm Ground-based retrieval Path-concentration SO2 PLSR Hyperspectral imaging 
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Notes

Acknowledgements

We thank the United States Department of Interior National Parks Service for authorizing the collection of the field data reported in this paper (Permit number HAVO-2015-SCI-0050). We thank Dr. Jacopo Taddeucci (Istituto Nazionale di Geofisica e Vulcanologia, Italy), Dr. Andrew Harris (Université Clermont Auvergne, Laboratoire Magmas et Volcans, France), and two anonymous reviewers for their most valuable comments on the manuscript, as their feedback led us to an improvement of the work. We also thank Dr. Matthew Blackett (Coventry University, UK), for his help with the field data collection, and Dr. Alexander Berk (Spectral Sciences Inc.) for his insightful suggestions regarding the use of MODTRAN5. This is HIGP publication number 2269 and SOEST publication number 1025.

Funding statement

'Funding for this work was provided by NASA’s Earth Science Technology Office (Instrument Incubator Program, NNX14AE61G).

Supplementary material

445_2017_1163_MOESM1_ESM.docx (9.6 mb)
ESM 1 (DOCX 9809 kb)

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Hawai’i Institute of Geophysics and PlanetologyUniversity of Hawai’i at MānoaHonoluluUSA
  2. 2.Department of Geology and Geophysics, School of Ocean and Earth Science and TechnologyUniversity of Hawai’i at MānoaHonoluluUSA

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