Twenty-one years of ASTER global thermal infrared (TIR) acquisitions provide a large amount of data for volcano monitoring. These data, with high spatial and spectral resolution, enable routine investigations of volcanoes in remote and inaccessible regions, including those with no ground-based monitoring. However, the dataset is too large to be manually analyzed on a global basis. Here, we systematically process the data over several volcanoes using a deep learning algorithm to automatically extract volcanic thermal anomalies. We explore the application of a Convolutional Neural Network (CNN), specifically UNET, to detect subtle to intense anomalies exploiting the spatial relationships of the volcanic features. We employ a supervised UNET network trained with the largest (1500) labeled dataset of ASTER TIR images from five different volcanoes, namely Etna (Italy), Popocatépetl (Mexico), Lascar (Chile), Fuego (Guatemala), and Kliuchevskoi (Russia). We show that our approach achieves high accuracy (93%) with excellent generalization capabilities. The effectiveness of our model for detecting the full range of thermal emission is shown for volcanoes with very different styles of activity and tested at Vulcano (Italy). The results demonstrate the potential applicability of the proposed approach to the development of automated thermal analysis systems at the global-scale using future TIR data such as the planned NASA SBG mission.

Hydrothermal systems can generate phreatic and/or phreatomagmatic explosions with little warning. Understanding the temporal and spatial evolution of geophysical and geochemical signals at hydrothermal systems is crucial for detecting precursory signs to unrest and to inform on hazard. Thermal signatures of such systems are poorly defined because data records are often too short or discrete compared to activity timescales, which can be decadal. La Fossa system of Vulcano has been monitored since the 1980s and entered a period of unrest in 2021. We assessed the thermal signature of La Fossa using ground- and satellite-based data with various temporal and spatial scales. While continuously-recording stations provided continuous but point-based measurements, fumarole field vent surveys and infrared images obtained from satellite-flown sensors (ASTER and VIIRS) allowed lower temporal resolution but synoptic records to be built. By integrating this multi-resolution data set, precursory signs of unrest could retrospectively be detected from February to June 2021. Intensity of all unrest metrics increased during summer 2021, with an onset over a few days in September 2021. By September, seismic, CO₂, SO₂ and other geochemical metrics also indicated unrest, leading Civil Protection to raise the alert level to yellow on October 1. Heat flux, having been 4 MW in May 2019, increasing to 90 MW by September, and peaking at 120 MW in March 2022. We convolved our thermal data sets with all other monitoring data to validate a Vulcano Fossa Unrest Index (VFUI), framework of which can be potentially applied to any hydrothermal system. The VFUI highlighted four stages of unrest, none of which were clear in any single data set: background, precursory, onset and unrest. Onset was characterized by sudden release of fluids, likely caused by failure of sealed zones that had become pressurized during the precursory phase that began possibly as early as February 2021. Unrest has been ongoing for more than 18 months, and may continue for several more years. Our understanding of this system behavior has been due to hindsight, but demonstrates how multiparametric surveys can track and forecast unrest. Graphical Abstract

Volcanic plume height is a key parameter in retrieving plume ascent and dispersal dynamics, as well as eruption intensity; all of which are crucial for assessing hazards to aircraft operations. One way to retrieve cloud height is the shadow technique. This uses shadows cast on the ground and the sun geometry to calculate cloud height. This technique has, however, not been frequently used, especially not with high-spatial resolution (30 m pixel) satellite data. On 26 October 2013, Mt Etna (Sicily, Italy) produced a lava fountain feeding an ash plume that drifted SW and through the approach routes to Catania international airport. We compared the proximal plume height time-series obtained from fixed monitoring cameras with data retrieved from a Landsat-8 Operational Land Imager image, with results being in good agreement. The application of the shadow technique to a single high-spatial resolution image allowed us to fully document the ascent and dispersion history of the plume–cloud system. We managed to do this over a distance of 60 km and a time period of 50 min, with a precision of a few seconds and vertical error on plume altitude of ±200 m. We converted height with distance to height with time using the plume dispersion velocity, defining a bent-over plume that settled to a neutral buoyancy level with distance. Potentially, the shadow technique defined here allows downwind plume height profiles and mass discharge rate time series to be built over distances of up to 260 km and periods of 24 h, depending on vent location in the image, wind speed, and direction.

Formalised elicitation of expert judgements has been used to help tackle several problematic societal issues, including volcanic crises and pandemic threats. We present an expert elicitation exercise for Piton de la Fournaise volcano, La Réunion island, held remotely in April 2021. This involved 28 experts from nine countries who considered a hypothetical effusive eruption crisis involving a new vent opening in a high-risk area. The tele-elicitation presented several challenges, but is a promising and workable option for application to future volcanic crises. Our exercise considered an “uncommon” eruptive scenario with a vent outside the present caldera and within inhabited areas, and provided uncertainty ranges for several hazard-related questions for such a scenario (e.g. probability of eruption within a defined timeframe; elapsed time until lava flow reaches a critical location, and other hazard management issues). Our exercise indicated that such a scenario would probably present very different characteristics compared to recent eruptions, and that it is fundamental to include well-prepared expert elicitations in updated civil protection evacuation plans to improve disaster response procedures.

Assessing Radiative Power (RP) output is essential for monitoring and understanding volcanic systems. While Mid‐Infrared channels are used to assess thermal outputs at volcanoes exhibiting effusive activity, Thermal‐InfraRed (TIR) bands are better suited for measuring moderate‐to‐low‐temperature (≲600 K) features, such as those associated with hydrothermal activity. However, failure to meet key assumptions in TIR‐based calculations results in up to a ∼90% RP underestimation of ≲600 K sources. We thus introduce the TIR‐based Volcanic Radiative Power () method to accurately retrieve RP from single‐band TIR (10.5–12 μm) spectral radiance at systems dominated by surface temperatures of ≲600 K, that is, crater lakes and fumarole fields, achieving an uncertainty of ±35%. Comparison with ground truth for Ruapehu, El Chichón, Taal, Vulcano, Puracé, Poás, and White Island demonstrates the accuracy of in quantifying thermal output and detecting subtle variations in volcanic activity. This exportable method will facilitate compilation of global RP inventories for moderate‐to‐low‐temperature volcanic systems.