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Open AccessArticle

Ground-Based Remote Sensing of Volcanic CO2 Fluxes at Solfatara (Italy)—Direct Versus Inverse Bayesian Retrieval

1
School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK
2
Istituto Nazionale di Geofisica e Vulcanologia—Sezione di Pisa, Via della Faggiola, 32, 56126 Pisa, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(1), 125; https://doi.org/10.3390/rs10010125
Received: 11 December 2017 / Revised: 12 January 2018 / Accepted: 16 January 2018 / Published: 18 January 2018
(This article belongs to the Section Atmosphere Remote Sensing)
CO2 is the second most abundant volatile species of degassing magma. CO2 fluxes carry information of incredible value, such as periods of volcanic unrest. Ground-based laser remote sensing is a powerful technique to measure CO2 fluxes in a spatially integrated manner, quickly and from a safe distance, but it needs accurate knowledge of the plume speed. The latter is often difficult to estimate, particularly for complex topographies. So, a supplementary or even alternative way of retrieving fluxes would be beneficial. Here, we assess Bayesian inversion as a potential technique for the case of the volcanic crater of Solfatara (Italy), a complex terrain hosting two major CO2 degassing fumarolic vents close to a steep slope. Direct integration of remotely sensed CO2 concentrations of these vents using plume speed derived from optical flow analysis yielded a flux of 717 ± 121 t day−1, in agreement with independent measurements. The flux from Bayesian inversion based on a simple Gaussian plume model was in excellent agreement under certain conditions. In conclusion, Bayesian inversion is a promising retrieval tool for CO2 fluxes, especially in situations where plume speed estimation methods fail, e.g., optical flow for transparent plumes. The results have implications beyond volcanology, including ground-based remote sensing of greenhouse gases and verification of satellite soundings. View Full-Text
Keywords: CO2; flux; Bayesian inversion; optical flow; volcanoes; magmatic degassing; volcanic gases CO2; flux; Bayesian inversion; optical flow; volcanoes; magmatic degassing; volcanic gases
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MDPI and ACS Style

Queißer, M.; Burton, M.; Granieri, D.; Varnam, M. Ground-Based Remote Sensing of Volcanic CO2 Fluxes at Solfatara (Italy)—Direct Versus Inverse Bayesian Retrieval. Remote Sens. 2018, 10, 125.

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