Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO₂ gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards.

We describe a new volcanic hotspot detection system, named Middle InfraRed Observation of Volcanic Activity (MIROVA), based on the analysis of infrared data acquired by the Moderate Resolution Imaging Spectroradiometer sensor (MODIS). MIROVA uses the middle infrared radiation (MIR), measured by MODIS, in order to detect and measure the heat radiation deriving from volcanic activity. The algorithm combines spectral and spatial principles, allowing the detection of heat sources from 1 megawatt (MW) to more than 10 gigawatt (GW). This provides a unique opportunity to: (i) recognize small-scale variations in thermal output that may precede the onset of effusive activity; (ii) track the advance of large lava flows; (iii) estimate lava discharge rates; (iv) identify distinct effusive trends; and, lastly, (v) follow the cooling process of voluminous lava bodies for several months. Here we show the results obtained from data sets spanning 14 years recorded at the Stromboli and Mt Etna volcanoes, Italy, and we investigate the above aspects at these two persistently active volcanoes. Finally, we describe how the algorithm has been implemented within an operational near-real-time processing chain that enables the MIROVA system to provide data and infrared maps within 1–4 h of the satellite overpass.

Flank instability and sector collapses, which pose major threats, are common on volcanic islands. On 22 Dec 2018, a sector collapse event occurred at Anak Krakatau volcano in the Sunda Strait, triggering a deadly tsunami. Here we use multiparametric ground-based and space-borne data to show that prior to its collapse, the volcano exhibited an elevated state of activity, including precursory thermal anomalies, an increase in the island’s surface area, and a gradual seaward motion of its southwestern flank on a dipping décollement. Two minutes after a small earthquake, seismic signals characterize the collapse of the volcano’s flank at 13:55 UTC. This sector collapse decapitated the cone-shaped edifice and triggered a tsunami that caused 430 fatalities. We discuss the nature of the precursor processes underpinning the collapse that culminated in a complex hazard cascade with important implications for the early detection of potential flank instability at other volcanoes.

Volcanic activity is always accompanied by the transfer of heat from the Earth’s crust to the atmosphere. This heat can be measured from space and its measurement is a very useful tool for detecting volcanic activity on a global scale. MIROVA (Middle Infrared Observation of Volcanic Activity) is an automatic volcano hot spot detection system, based on the analysis of MODIS data (Moderate Resolution Imaging Spectroradiometer). The system is able to detect, locate and quantify thermal anomalies in near real-time, by providing, on a dedicated website (www.mirovaweb.it), infrared images and thermal flux time-series on over 200 volcanoes worldwide. Thanks to its simple interface and intuitive representation of the data, MIROVA is currently used by several volcano observatories for daily monitoring activities and reporting. In this paper, we present the architecture of the system and we provide a state of the art on satellite thermal data usage for operational volcano monitoring and research. In particular, we describe the contribution that the thermal data have provided in order to detect volcanic unrest, to forecast eruptions and to depict trends and patterns during eruptive crisis. The current limits and requirements to improve the quality of the data, their distribution and interpretation are also discussed, in the light of the experience gained in recent years within the volcanological community. The results presented clearly demonstrate how the open access of satellite thermal data and the sharing of derived products allow a better understanding of ongoing volcanic phenomena, and therefore constitute an essential requirement for the assessment of volcanic hazards.

Long‐lasting, hybrid eruptions can be of complex description and classification, especially when associated with multiple eruptive styles and multiple products. The 2021 Tajogaite eruption of La Palma, Canary Islands, was associated with a magma‐gas decoupled system that resulted in the simultaneous emission of lava flows and tephra plumes from various vents. Even though the tephra blanket (∼2 × 10⁷ m³) represents only 7%–16% of the total erupted volume, it provides fundamental insights into the overall eruptive dynamics. Tephra was mostly dispersed NE‐SW due to a complex regional and local wind patterns and was subdivided into 3 units and 11 layers that well correlate at different distances from the vent and with both tremor data and lava emission rate. While plume height varied at the temporal scale of a few hours, the average mass eruption rate associated with the tephra blanket of the different units remained relatively constant (∼3–4 × 10³ kg s⁻¹). In contrast, the emission rate of lava largely increased after the first week and remained higher than the overall emission of tephra throughout the whole eruption (average value of ∼6 × 10⁴ kg s⁻¹). Based on a detailed characterization of the tephra blanket in combination with atmospheric wind, tremor, and lava emission trend, we demonstrate the need of (a) multidisciplinary strategies for the description of hybrid eruptions that account for both the duration of individual phases and the quantification of the mass of multiple products, and of (b) dedicated ash dispersal forecasting strategies that account for the frequent variations of eruptive and atmospheric conditions.

Volcanoes are hazardous to local and global populations, but only a fraction are continuously monitored by ground‐based sensors. For example, in Latin America, more than 60% of Holocene volcanoes are unmonitored, meaning long‐term multiparameter data sets of volcanic activity are rare and sparse. We use satellite observations of degassing, thermal anomalies, and surface deformation spanning 17 years at 47 of the most active volcanoes in Latin America and compare these data sets to ground‐based observations archived by the Global Volcanism Program. This first comparison of multisatellite time series on a regional scale provides information regarding volcanic behavior during, noneruptive, pre‐eruptive, syneruptive, and posteruptive periods. For example, at Copahue volcano, deviations from background activity in all three types of satellite measurements were manifested months to years in advance of renewed eruptive activity in 2012. By quantifying the amount of degassing, thermal output, and deformation measured at each of these volcanoes, we test the classification of these volcanoes as open or closed volcanic systems. We find that ~28% of the volcanoes do not fall into either classification, and the rest show elements of both, demonstrating a dynamic range of behavior that can change over time. Finally, we recommend how volcano monitoring could be improved through better coordination of available satellite‐based capabilities and new instruments.