Thermal Remote Sensing of Volcanic Activity

Thermal remote sensing of volcanic activity employs satellite and airborne sensors to detect and monitor heat emissions from active and restless volcanoes. By measuring the thermal infrared radiation emitted by Earth’s surface, researchers can identify temperature anomalies linked to magma movement, lava effusion, fumarolic fields and hydrothermal systems. Advances in sensor design have produced instruments with increasingly fine spatial resolution, rapid revisit times and multi-spectral capabilities, enabling continuous surveillance of remote or inaccessible volcanoes worldwide. Data from platforms such as ASTER, Landsat, Sentinel-3 and geostationary satellites support quantitative estimation of radiative heat flux, the mapping of lava flow extents and the assessment of pre-eruptive thermal precursors. Integration with ground-based observations and numerical models enhances forecasts of eruption timing and style, while machine learning algorithms have begun to automate anomaly detection across vast archives of imagery. The global significance of thermal remote sensing lies in its capacity to extend hazard monitoring to regions lacking ground networks, to characterise subtle long-term thermal trends and to improve the resilience of at-risk communities by informing early warning systems. Emerging multisensor approaches, combining thermal data with gas, deformation and optical measurements, are refining our understanding of volcanic processes and supporting ever more timely and precise risk assessments.

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Thermal Remote Sensing of Volcanic Activity publication trend

The graph below shows the total number of articles in thermal remote sensing of volcanic activity across all publications each year (not limited to Nature Index journals).

Technical terms

Thermal infrared (TIR): Portion of the infrared spectrum (approximately 8–14 µm) in which emitted surface radiation is measured to infer temperature.

Emissivity: Efficiency with which a surface emits thermal radiation relative to a blackbody, influencing recorded radiance.

Thermal anomaly: Localised deviation in surface temperature, often indicating volcanic or hydrothermal activity.

Radiative heat flux: Rate of thermal energy emitted per unit area, used to estimate effusion rates and eruptive intensity.

Spatial resolution: Ground area represented by a single pixel in an image, determining the smallest detectable feature.

Convolutional Neural Network (CNN): Deep learning architecture designed to extract spatial patterns from imagery, applied here to automate anomaly detection.

References

1. Statistical retrieval of volcanic activity in long time series orbital data: Implications for forecasting future activity. Remote Sensing of Environment (2023).
2. Detection of Subtle Thermal Anomalies: Deep Learning Applied to the ASTER Global Volcano Dataset. IEEE Transactions on Geoscience and Remote Sensing (2023).
3. Detection of Geothermal Anomalies in Hydrothermal Systems Using ASTER Data: The Caldeiras da Ribeira Grande Case Study (Azores, Portugal). Sensors (2023).