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.

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.

Explosive volcanic eruptions can eject large amounts of ash into the atmosphere, posing a serious threat to populations living near the volcano. The abrupt occurrence of such events requires a rapid response and proper volcanic hazard evaluation. Current monitoring procedures still require human intervention, which often results in significant delays between the occurrence of an eruption and notifications being dispatched. We show how dedicated infrasound array processing can be used to detect and notify the authorities, automatically and in real time, of the onset of explosive eruptions. Conceptually, our method relies on the strong coupling between infrasound and the explosive process, and it is not based on probabilistic considerations but on the ability infrasound has to detect the early stage of the explosive phase. This procedure has been tested for the last 8 years, and it is currently applied to issue early warnings for explosive eruptions at Etna Volcano. We show that the system is able to provide a prealert ~1 hr before the eruption, and it has a 96.6% success rate, with only 1.7% false positive alerts and no false negative alerts. This is, to our knowledge, the first example of an operational early warning system totally based on an unsupervised algorithm that provides automatic notifications of eruptions to a government agency. We show that the same early warning concept might be applicable to arrays at large distances (>500 km), suggesting that infrasound could be successfully used to issue automatic notifications of ongoing eruptions at regional to global scales.

In the satellite thermal remote sensing, the new generation of sensors with high-spatial resolution SWIR data open the door to an improved constraining of thermal phenomena related to volcanic processes, with strong implications for monitoring applications. In this paper, we describe a new hot-spot detection algorithm developed for SENTINEL-2/MSI data that combines spectral indices on the SWIR bands 8a-11-12 (with a 20-meter resolution) with a spatial and statistical analysis on clusters of alerted pixels. The algorithm is able to detect hot-spot-contaminated pixels (S2Pix) in a wide range of environments and for several types of volcanic activities, showing high accuracy performances of about 1% and 94% in averaged omission and commission rates, respectively, underlining a strong reliability on a global scale. The S2-derived thermal trends, retrieved at eight key-case volcanoes, are then compared with the Volcanic Radiative Power (VRP) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) and processed by the MIROVA (Middle InfraRed Observation of Volcanic Activity) system during an almost four-year-long period, January 2016 to October 2019. The presented data indicate an overall excellent correlation between the two thermal signals, enhancing the higher sensitivity of SENTINEL-2 to detect subtle, low-temperature thermal signals. Moreover, for each case we explore the specific relationship between S2Pix and VRP showing how different volcanic processes (i.e., lava flows, domes, lakes and open-vent activity) produce a distinct pattern in terms of size and intensity of the thermal anomaly. These promising results indicate how the algorithm here presented could be applicable for volcanic monitoring purposes and integrated into operational systems. Moreover, the combination of high-resolution (S2/MSI) and moderate-resolution (MODIS) thermal timeseries constitutes a breakthrough for future multi-sensor hot-spot detection systems, with increased monitoring capabilities that are useful for communities which interact with active volcanoes.

The mass discharge rate is a key parameter for initializing volcanic ash dispersal models. Commonly used empirical approaches derive the discharge rate by the plume height as estimated by remote sensors. A novel approach based on the combination of weather radar observations and thermal camera imagery is presented here. It is based on radar ash concentration estimation and the retrieval of the vertical exit velocities of the explosive cloud using thermal camera measurements. The applied radar retrieval methodology is taken from a revision of previously presented work. Based on the analysis of four eruption events of the Mount Etna volcano (Sicily, Italy) that occurred in December 2015, the proposed methodology is tested using observations collected by three radar systems (at C and X band) operated by the Italian Department of Civil Protection. The total erupted mass was estimated to be about 9·10⁹ kg and 2.4·10⁹ kg for the first and second events, respectively, while it was about 1.2·10⁹ kg for both the last two episodes. The comparison with empirical approaches based on radar‐retrieved plume height shows a reasonably good agreement. Additionally, the comparative analysis of the polarimetric radar measurements provides interesting information on the vertical structure of the ash plume, including the size of the eruption column and the height of the gas thrust region.