Filizzola, Carolina

In the original article [1], there were some mistakes in Figures 4 and 8, and Sections ‘Abstract’, ‘Discussion’, and ‘Conclusions’. The correct contents appears below. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original article has been updated.

On 16 February 2021, an eruptive paroxysm took place at Mt. Etna (Sicily, Italy), after continuous Strombolian activity recorded at summit craters, which intensified in December 2020. This was the first of 17 short, but violent, eruptive events occurring during February–April 2021, mostly at a time interval of about 2–3 days between each other. The paroxysms produced lava fountains (up to 1000 m high), huge tephra columns (up to 10–11 km above sea level), lava and pyroclastic flows, expanding 2–4 km towards East and South. The last event, which was characterised by about 3 days of almost continuous eruptive activity (30 March–1 April), generated the most lasting lava fountain (8–9 h). During some paroxysms, volcanic ash led to the temporary closure of the Vincenzo Bellini Catania International Airport. Heavy ash falls then affected the areas surrounding the volcano, in some cases reaching zones located hundreds of kilometres away from the eruptive vent. In this study, we investigate the Mt. Etna paroxysms mentioned above through a multi-platform satellite system. Results retrieved from Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Spinning Enhanced Visible and Infrared Imager (SEVIRI), starting from outputs of the Robust Satellite Techniques for Volcanoes (RSTVOLC), indicate that the 17th paroxysm (31 March–1 April) was the most powerful, with values of radiative power estimated around 14 GW. Moreover, by the analysis of SEVIRI data, we found that the 5th and 17th paroxysms were the most energetic. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), providing shortwave infrared (SWIR) data at 20/30 m spatial resolution, enabled an accurate localisation of active vents and the mapping of the areas inundated by lava flows. In addition, according to the Normalized Hotspot Indices (NHI) tool, the 1st and 3rd paroxysm (18 and 28 February) generated the largest thermal anomaly at Mt. Etna after June 2013, when Landsat-8 OLI data became available. Despite the impact of clouds/plumes, pixel saturation, and other factors (e.g., satellite viewing geometry) on thermal anomaly identification, the used multi-sensor approach allowed us to retrieve quantitative information about the 17 paroxysms occurring at Mt. Etna. This approach could support scientists in better interpreting changes in thermal activity, which could lead to future and more dangerous eruptions.

On 3 July 2019 a rapid sequence of paroxysmal explosions at the summit craters of Stromboli (Aeolian-Islands, Italy) occurred, followed by a period of intense Strombolian and effusive activity in July, and continuing until the end of August 2019. We present a joint analysis of multi-sensor infrared satellite imagery to investigate this eruption episode. Data from the SpinningEnhanced-Visible-and-InfraRed-Imager (SEVIRI) was used in combination with those from the Multispectral-Instrument (MSI), the Operational-Land-Imager (OLI), the Advanced-Very HighResolution-Radiometer (AVHRR), and the Visible-Infrared-Imaging-Radiometer-Suite (VIIRS). The analysis of infrared SEVIRI-data allowed us to detect eruption onset and to investigate short-term variations of thermal volcanic activity, providing information in agreement with that inferred by nighttime-AVHRR-observations. By using Sentinel-2-MSI and Landsat-8-OLI imagery, we better localized the active lava-flows. The latter were quantitatively characterized using infrared VIIRSdata, estimating an erupted lava volume of 6.33×10±3.17×10 m 3 and a mean output rate of 1.26 ± 0.63 m3/s for the July/August 2019 eruption period. The estimated mean-output-rate was higher than the ones in the 2002–2003 and 2014 Stromboli effusive eruptions, but was lower than in the 2007-eruption. These results confirmed that a multi-sensor-approach might provide a relevant contribution to investigate, monitor and characterize thermal volcanic activity in high-risk areas.

Several authors claim a space-time correlation between increases in Earth’s emitted Thermal Infra-Red (TIR) radiation and earthquake occurrence. The main problems of such studies regard data analysis and interpretation, which are often done without a validation/confutation control. In this context, a robust data analysis technique (RST, i.e. Robust Satellite Techniques) is proposed which permits a statistically based definition of TIR «anomaly » and uses a validation/confutation approach. This technique was already applied to satellite TIR surveys in seismic regions for about twenty earthquakes that occurred in the world. In this work RST is applied for the first time to a time sequence of seismic events. Nine years of Meteosat TIR observations have been analyzed to characterize the unperturbed TIR signal behaviour at specific observation times and locations. The main seismic events of the October 1997 Umbria-Marche sequence have been considered for validation, and relatively unperturbed periods (no earthquakes with Mb ≥ 4) were taken for confutation purposes. Positive time-space persistent TIR anomalies were observed during seismic periods, generally overlapping the principal tectonic lineaments of the region and sometimes focusing on the vicinity of the epicentre. No similar (in terms of relative intensity and space-time persistence) TIR anomalies were detected during seismically unperturbed periods.

In the last few years, Robust Satellite data analysis Techniques (RST) have been proposed which significantly improved present capabilities to investigate possible relations between TIR signal fluctuations and earthquake occurrence. This paper, starting from a critical survey of results achieved by applying different RST-based algorithms to different satellite sensors to approximately ten earthquakes (two of them are discussed here for the first time) which occurred in three different continents, tries to offer a first assessment of main achievements, residual limits and perspectives of such studies. Even if it is still not possible to relate (or to exclude) observed anomalous TIR transients definitely to impending earthquakes, such studies demonstrate at least: a) the strong improvement of S/N ratio achievable moving from polar to geostationary satellites; b) the further S/N improvement achievable by using TIR sensors which also offer split-window possibilities; c) the crucial role played by a space-time persistence test to select TIR anomalies candidate to be associated to impending earthquakes; d) the possibility of identifying and correctly discarding TIR anomalies related to clouds and to image navigation errors; e) the scarce importance of spatial resolution of observations which encourages the use of passive MW sensors which are less affected by atmospheric conditions.

Nowadays, satellite remote sensing is an important tool for volcanic activity monitoring, thanks to several operational satellite platforms providing data everywhere with high observational frequencies and generally at low cost. Among different techniques available, an advanced satellite method, named RST (Robust Satellite Technique), based on the multitemporal analysis of satellite data, has shown a high capability in volcanic activity monitoring. This approach has proved capable of identifying and tracking volcanic ash cloud and of correctly detecting and monitoring volcanic thermal anomalies. This paper analyzes some recent results, obtained applying this approach to the last eruptive events of Mt. Etna using both polar and geostationary satellites. In particular, for the first time, this approach is implemented on the present geostationary platform MSG-SEVIRI, with 15 min of temporal resolution. Preliminary results, together with a future potential of this implementation, are shown and discussed. Moreover, a differential RST index in time domain is also proposed for near real-time application, as a possible contribution to the development of an efficient early warning satellite system for volcanic hazard mitigation.

Several satellite techniques have been proposed to monitor events related to seismic and volcanic activity. A selfadaptive approach (RAT, Robust AVHRR Techniques) has recently been proposed which seems able to recognise space-time anomalies, differently related to such events, also in the presence of highly variable contributions from atmospheric (transmittance), surface (emissivity and morphology) and observational (time/season, but also solar and satellite zenithal angles) conditions. On the basis of NOAA-AVHRR data, the RAT aprroach has already been applied to Mount Etna volcanic ash cloud monitoring in daytime, and to seismic area monitoring in Southern Italy. This paper presents the theoretical basis for the extension of RAT approach also to nighttime volcanic ash cloud detection, together with its possible implementation to lava flow monitoring. One example of successful forecasting (few days before) of a new lava vent opening during the Mount Etna eruption of July 2001 will be discussed in some detail. Progress on the use of the same approach on seismically active area monitoring will be discussed by comparison with previous results achieved on the Irpinia-Basilicata earthquake (MS = 6.9), which occurred on November 23rd 1980 in Southern Italy.

In this work the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) hyperspectral data, acquired during aerial campaigns made in 1998 over the Pollino National Park in the framework of the «Progetto Pollino», have been used to set up a supervised technique devoted to identify the presence of selected rocky outcrops. Tests have been performed over an extended area characterised by a complex orography. Within this area, serpentinite was chosen as a test-rock because it is present in isolated outcrops, distributed all over the test-area, besides subtending important problems of environmental nature as it contains asbestos. Geological information, coming from field observations or geological maps, was used to trigger the algorithms and as ground truth for its validation. Two spectral analysis techniques, SAM (Spectral Angle Mapper) and LSU (Linear Spectral Unmixing), have been applied and their results n combined to automatically identify serpentinite outcrops and, in some cases, to mark its boundaries. The approach used in this work is characterised by simplicity (no atmosphere and illumination corrections were performed on MIVIS data), robustness (material of interest is identified for certainty) and intrinsic exportability (the method proposed can be applied on different geographic areas and, in theory, to identify any kind of material because no datum about atmospheric and illumination conditions is required).