SSTAR: a user-friendly framework for detecting and monitoring subtle thermal precursors to volcanic eruptions—application to Shishaldin, Alaska

In recent decades, the monitoring of volcanoes has been revolutionized by the launch of Earth-observing satellites and advances in thermal infrared remote sensing. These developments have revealed a wide range of thermal responses of volcanic surfaces to subsurface processes, even demonstrating that eruptions are often preceded by measurable thermal anomalies. This recognition highlights the need for robust tools to systematically detect and track such anomalies, making full use of existing satellite datasets and maximizing the value of current instruments in orbit. To address this challenge, we present the Subtle Surface Thermal Anomalies Recognizer (SSTAR), a versatile and user-friendly application designed to analyze diffuse thermal anomalies, i.e., subtle thermal unrest (~ 1 K) across large areas (several km2). SSTAR leverages data from NASA’s Terra and Aqua satellites, which host the Moderate Resolution Imaging Spectroradiometers (MODIS), and builds upon a robust statistical framework. By processing pixel-level data, SSTAR tracks the temporal evolution of diffuse thermal anomalies at specific target sites and maps their spatiotemporal distribution across extended areas. Key features include filtering tools that distinguish between long-term (years) and short-term (weeks) anomalies, as well as uncertainty quantification using bootstrapping. The application is standalone, features an interactive interface for streamlined analysis, and is accessible to newcomers to satellite-based thermal remote sensing. At the same time, specialized users can customize the underlying scripts for other specific research needs. As a demonstration, we apply SSTAR to Shishaldin volcano (Alaska), revealing the emergence of significant thermal anomalies around the summit crater and flanks prior to eruptions. We envision SSTAR as a valuable resource for studying subtle thermal unrest at active volcanoes and hydrothermal systems, where the detection of faint and spatially coherent anomalies may help identify subsurface fluid pathways. Its flexible design enables integration with additional satellite datasets, positioning SSTAR as a forward-looking tool for advancing space-based volcanic thermal monitoring. Building on this capability, daily updated diffuse thermal anomalies are provided for target volcanoes through an open web platform hosted at Geosciences Barcelona—CSIC ( https://sstar.geo3bcn.csic.es/ ), to support surveillance agencies and expert committees in alert-level assessments.

Authors

Institutions

Publication Details

Journal
Earth Planets and Space
Published
2026-07-23
DOI
https://doi.org/10.1186/s40623-026-02497-6
Primary Topic
Geological and Geochemical Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SSTAR: a user-friendly framework for detecting and monitoring subtle thermal precursors to volcanic eruptions—application to Shishaldin, Alaska

Társilo Girona, Laure Brenot
Earth Planets and Space
Geological and Geochemical Analysis
article

SSTAR: a user-friendly framework for detecting and monitoring subtle thermal precursors to volcanic eruptions—application to Shishaldin, Alaska

Társilo Girona, Laure Brenot
article en

Abstract

In recent decades, the monitoring of volcanoes has been revolutionized by the launch of Earth-observing satellites and advances in thermal infrared remote sensing. These developments have revealed a wide range of thermal responses of volcanic surfaces to subsurface processes, even demonstrating that eruptions are often preceded by measurable thermal anomalies. This recognition highlights the need for robust tools to systematically detect and track such anomalies, making full use of existing satellite datasets and maximizing the value of current instruments in orbit. To address this challenge, we present the Subtle Surface Thermal Anomalies Recognizer (SSTAR), a versatile and user-friendly application designed to analyze diffuse thermal anomalies, i.e., subtle thermal unrest (~ 1 K) across large areas (several km2). SSTAR leverages data from NASA’s Terra and Aqua satellites, which host the Moderate Resolution Imaging Spectroradiometers (MODIS), and builds upon a robust statistical framework. By processing pixel-level data, SSTAR tracks the temporal evolution of diffuse thermal anomalies at specific target sites and maps their spatiotemporal distribution across extended areas. Key features include filtering tools that distinguish between long-term (years) and short-term (weeks) anomalies, as well as uncertainty quantification using bootstrapping. The application is standalone, features an interactive interface for streamlined analysis, and is accessible to newcomers to satellite-based thermal remote sensing. At the same time, specialized users can customize the underlying scripts for other specific research needs. As a demonstration, we apply SSTAR to Shishaldin volcano (Alaska), revealing the emergence of significant thermal anomalies around the summit crater and flanks prior to eruptions. We envision SSTAR as a valuable resource for studying subtle thermal unrest at active volcanoes and hydrothermal systems, where the detection of faint and spatially coherent anomalies may help identify subsurface fluid pathways. Its flexible design enables integration with additional satellite datasets, positioning SSTAR as a forward-looking tool for advancing space-based volcanic thermal monitoring. Building on this capability, daily updated diffuse thermal anomalies are provided for target volcanoes through an open web platform hosted at Geosciences Barcelona—CSIC ( https://sstar.geo3bcn.csic.es/ ), to support surveillance agencies and expert committees in alert-level assessments.

Earth Planets and SpaceVol. 78(1)
Université Libre de Bruxelles (BE), University of Alaska Fairbanks (US), Geociencias Barcelona (ES)
National Aeronautics and Space Administration
Climate action
Openalex Percentile: Top 11%
Geological and Geochemical Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.