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\begin{document}

\title{Statistical analysis of the two-decade ASTER archive: Quantitative
retrievals of volcanic thermal and gas emissions}



\author[1]{Tyler Leggett}%
\author[1]{Michael Ramsey}%
\author[2]{Claudia Corradino}%
\affil[1]{University of Pittsburgh}%
\affil[2]{INGV - National Institute of Geophysics and Volcanology}%


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  \date{June 18, 2024}


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\begin{abstract}
Detailed analysis of volcanic thermal and gas emissions over time can
constrain subsurface processes throughout the pre- and post-eruption
phases. Time series analyses are commonly applied to high temporal
datasets like the Moderate Resolution Imaging Spectroradiometer (MODIS);
however, this is the first study using the entire Advanced Spaceborne
Thermal Emission and Reflection Radiometer (ASTER) twenty-plus year
archive. The ASTER archive presents a unique opportunity to quantify
volcanic precursors and processes. The spatial, spectral, and
radiometric resolution of its thermal infrared (TIR) subsystem allows
detection of very low-magnitude surface temperature anomalies and
passively emitted small gas plumes. We developed a new statistical
algorithm to automatically detect these subtle anomalies and applied it
to five recently active volcanoes with well-documented eruptions: Taal
(Philippians), Popocat\selectlanguage{ngerman}épetl (Mexico), Mt. Etna (Italy), Fuego
(Guatemala), and Kluichevskoi (Russia). More than 3,300 ASTER level-1
terrain corrected (L1T), registered, radiance-at-sensor images were
downloaded from the NASA EARTHDATA website. These were screened for
significant summit cloud coverage, which removed approximately 25\% of
scenes. The remaining were converted to brightness temperature and a
median background temperature per scene was determined from an annulus
around the active crater to produce the temperature above background.
The algorithm creates a rejection criterion value defined by the median
absolute deviation to identify the thermal anomalies. The size and
intensity of these anomalies as well as the detection, composition,
emission rate of small plumes are retrieved one year prior to the known
eruptions for each volcano to identify all precursory signals. The
results of this study have the dual purpose of constraining
volcanological processes that lead to eruptions as well as providing
training data for machine learning modeling. Machine learning is an
effective and well-established technique that provides rapid
classification of volcanic activity such as thermal anomalies that
exceed a certain size and/or intensity. The comparison of these two
approaches is documented in a companion abstract in this session.%
\end{abstract}\selectlanguage{ngerman}%



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