Next Article in Journal
Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer
Previous Article in Journal
The Spatial Different Order Derivative Method of Gravity and Magnetic Anomalies for Source Distribution Inversion
Previous Article in Special Issue
Pyroclastic Density Current Hazard Assessment and Modeling Uncertainties for Fuego Volcano, Guatemala
 
 
Article

A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements

1
Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison, 1225 W. Dayton St., Madison, WI 53706, USA
2
Center for the Study of Active Volcanoes (CSAV), University of Hawai’i at Hilo, 200 W. Kawili Street, Hilo, HI 96720, USA
3
National Oceanic and Atmospheric Administration (NOAA), 1225 W. Dayton St., Madison, WI 53706, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Ciro Del Negro, Michael Ramsey, Alexis Hérault and Gaetana Ganci
Remote Sens. 2021, 13(5), 966; https://doi.org/10.3390/rs13050966
Received: 3 February 2021 / Revised: 24 February 2021 / Accepted: 27 February 2021 / Published: 4 March 2021
Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to estimate the SO2 source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional SO2 datasets from ultraviolet (UV) sensors lack altitude information, which must be assumed, to correctly calibrate the SO2 data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded SO2 data. We then fit a solution to the general time-averaged dispersion of SO2 from a point source. We demonstrate these techniques using SO2 data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This SO2 data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR SO2 data can fill an important data gap in the UV-based record: estimating SO2 emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used. View Full-Text
Keywords: SO2 emissions; computer vision; time-averaged dispersion model; CrIS; JPSS SO2 emissions; computer vision; time-averaged dispersion model; CrIS; JPSS
Show Figures

Graphical abstract

MDPI and ACS Style

Hyman, D.M.R.; Pavolonis, M.J.; Sieglaff, J. A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements. Remote Sens. 2021, 13, 966. https://doi.org/10.3390/rs13050966

AMA Style

Hyman DMR, Pavolonis MJ, Sieglaff J. A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements. Remote Sensing. 2021; 13(5):966. https://doi.org/10.3390/rs13050966

Chicago/Turabian Style

Hyman, David M.R., Michael J. Pavolonis, and Justin Sieglaff. 2021. "A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements" Remote Sensing 13, no. 5: 966. https://doi.org/10.3390/rs13050966

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop