The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2A/2B and Landsat 8 satellites, thanks to their features especially in terms of spatial/spectral resolution, represents two important instruments for investigating thermal volcanic activity from space. In this study, we used data from those sensors to test an original multichannel algorithm, which aims at mapping volcanic thermal anomalies at a global scale. The algorithm, named Normalized Hotspot Indices (NHI), combines two normalized indices, analyzing near infrared (NIR) and short wave infrared (SWIR) radiances, to identify hotspot pixels in daylight conditions. Results, achieved studying a number of active volcanoes located in different geographic areas and characterized by a different eruptive behavior, demonstrated the NHI capacity in mapping both subtle and more intense volcanic thermal anomalies despite some limitations (e.g., missed detections because of clouds/volcanic plumes). In addition, the study shows that the performance of NHI might be further increased using some additional spectral/spatial tests, in view of a possible usage of this algorithm within a known multi-temporal scheme of satellite data analysis. The low processing times and the straight forth exportability to data from other sensors make NHI, which is sensitive even to other high temperature sources, suited for mapping hot volcanic targets integrating information provided by current and well-established satellite-based volcanoes monitoring systems.

Real-time integration of multi-parametric observations is expected to accelerate the process toward improved, and operationally more effective, systems for time-Dependent Assessment of Seismic Hazard (t-DASH) and earthquake short-term (from days to weeks) forecast. However, a very preliminary step in this direction is the identification of those parameters (chemical, physical, biological, etc.) whose anomalous variations can be, to some extent, associated with the complex process of preparation for major earthquakes. In this paper one of these parameters (the Earth’s emitted radiation in the Thermal InfraRed spectral region) is considered for its possible correlation withM ≥ 4 earthquakes occurred in Greece in between 2004 and 2013. The Robust Satellite Technique (RST) data analysis approach and Robust Estimator of TIR Anomalies (RETIRA) index were used to preliminarily define, and then to identify, significant sequences of TIR anomalies (SSTAs) in 10 years (2004–2013) of daily TIR images acquired by the Spinning Enhanced Visible and Infrared Imager on board the Meteosat Second Generation satellite. Taking into account the physical models proposed for justifying the existence of a correlation among TIR anomalies and earthquake occurrences, specific validation rules (in line with the ones used by the Collaboratory for the Study of Earthquake Predictability—CSEP—Project) have been defined to drive a retrospective correlation analysis process. The analysis shows that more than 93 % of all identified SSTAs occur in the prefixed space–time window around (M ≥ 4) earthquake's time and location of occurrence with a false positive rate smaller than 7 %. Molchan error diagram analysis shows that such a correlation is far to be achievable by chance notwithstanding the huge amount of missed events due to frequent space/time data gaps produced by the presence of clouds over the scene. Achieved results, and particularly the very low rate of false positives registered on a so long testing period, seems already sufficient (at least) to qualify TIR anomalies (identified by RST approach and RETIRA index) among the parameters to be considered in the framework of a multi-parametric approach to t-DASH.

Space-time fluctuations of Earth's emitted Thermal Infrared (TIR) radiation have been observed from satellite months to weeks before earthquakes occurrence. The general RST approach has been proposed in order to discriminate normal (i.e. related to the change of natural factor and/or observation conditions) TIR signal fluctuations from anomalous signal transient possibly associated to earthquake occurrence. In this work RST approach is applied to the Abruzzo 6 April 2009 event (M_L=5.8) by using for the first time MSG-SEVIRI (Meteosat Second Generation -Spinning Enhanced Visible and Infrared Imager) thermal infrared observations. A validation/confutation analysis has been performed in order to verify the presence/absence of anomalous space-time TIR transients in the presence/absence of significant seismic activity. March–April 2009 has been analyzed for validation purposes. Relatively unperturbed periods (no earthquakes with M_L≥5) have been taken for confutation. A specific TIR anomalies space-time persistence analysis as well as a cloud coverage distribution test have been introduced in order to eliminate artifacts and outliers both in the validation and confutation phases. Preliminary results show clear differences in TIR anomalies occurrence during the periods used for validation and confutation purposes. Quite clear TIR anomalies appear also to mark main tectonic lines related to the preparatory phases of others, low magnitude (M_L~4) earthquakes, occurred in the area.

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.