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Ground-based infrared cameras can be used effectively and safely to provide quantitative information about small to moderate-sized volcanic eruptions. This study describes an infrared camera that has been used to measure emissions from the Mt. Etna and Stromboli (Sicily, Italy) volcanoes. The camera provides calibrated brightness temperature images in a broadband (8–14 µm) channel that is used to determine height, plume ascent rate and volcanic cloud/plume temperature and emissivity at temporal sampling rates of up to 1 Hz. The camera can be operated in the field using a portable battery and includes a microprocessor, data storage and WiFi. The processing and analyses of the data are described with examples from the field experiments. The updraft speeds of the small eruptions at Stromboli are found to decay with a timescale of ∼10 min and the volcanic plumes reach thermal equilibrium within ∼2 min. A strong eruption of Mt. Etna on 1 April 2021 was found to reach ∼9 km, with ascent speeds of 10–20 ms−1. The plume, mostly composed of the gases CO2, water vapour and SO2, became bent over by the prevailing winds at high levels, demonstrating the need for multiple cameras to accurately infer plume heights.

From December 2020 to February 2022, 66 lava fountains (LF) occurred at Etna volcano (Italy). Despite their short duration (an average of about two hours), they produced a strong impact on human life, environment, and air traffic. In this work, the measurements collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board Meteosat Second Generation (MSG) geostationary satellite, are processed every 15 min to characterize the volcanic clouds produced during the activities. In particular, a quantitative estimation of volcanic cloud top height (VCTH) and ash/ice/SO2 masses’ time series are obtained. VCTHs are computed by integrating three different retrieval approaches based on coldest pixel detection, plume tracking, and HYSPLIT models, while particles and gas retrievals are realized simultaneously by exploiting the Volcanic Plume Retrieval (VPR) real-time procedure. The discrimination between ashy and icy pixels is carried out by applying the Brightness Temperature Difference (BTD) method with thresholds obtained by making specific Radiative Transfer Model simulations. Results indicate a VCTH variation during the entire period between 4 and 13 km, while the SO2, ash, and ice total masses reach maximum values of about 50, 100, and 300 Gg, respectively. The cumulative ash, ice, and SO2 emitted from all the 2020–2022 LFs in the atmosphere are about 750, 2300, and 670 Gg, respectively. All the retrievals indicate that the overall activity can be grouped into 3 main periods in which it passes from high (December 2020 to March 2021), low (March to June 2021), and medium/high (June 2021 to February 2022). The different products have been validated by using TROPOspheric Monitoring Instrument (TROPOMI) polar satellite sensor, Volcano Observatory Notices for Aviation (VONA) bulletins, and by processing the SEVIRI data considering a different and more accurate retrieval approach. The products’ cross-comparison shows a generally good agreement, except for the SO2 total mass in case of high ash/ice content in the volcanic cloud.

Satellite microwave (MW) and millimetre-wave (MMW) passive sensors can be used to detect volcanic clouds because of their sensitivity to larger volcanic particles (i.e., size bigger than 20 µm). In this work, we combine the MW-MMW observations with thermal-infrared (TIR) radiometric data from the Low Earth Orbit (LEO) spectroradiometer to have a complete characterisation of volcanic plumes. We describe new physical-statistical methods, which combine machine learning techniques, aimed at detecting and retrieving volcanic clouds of two highly explosive eruptions: the 2014 Kelud and 2015 Calbuco test cases. For the detection procedure, we compare the well-known split-window methods with a machine learning algorithm named random forest (RF). Our work highlights how the machine learning method is suitable to detect volcanic clouds using different spectral signatures without fixing a threshold. Moreover, the RF model allows images to be automatically processed with promising results (90% of the area correctly identified). For the retrieval procedure of the mass of volcanic particles, we consider two methods, one based on the maximum likelihood estimation (MLE) and one using the neural network (NN) architecture. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands. Summing the MW-MMW and TIR estimates, we obtain the following masses: 1.11 ± 0.40 × 1011 kg (MLE method) and 1.32 ± 0.47 × 1011 kg (NN method) for Kelud; 4.48 ± 1.61 × 1010 kg (MLE method) and 4.32 ± 1.56 × 1010 kg (NN method) for Calbuco. This work shows how machine learning techniques can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near-source volcanic clouds.

In this study, we focus on the eruption of Mount Etna on Christmas 2018, which emitted great amounts of SO2 from 24th to 30th December into the free troposphere. Simulations based on two different estimations of SO2 emission fluxes are conducted with the chemistry-transport model MOCAGE in order to study the impact of these estimations on the volcanic plume modeling. The two flux emissions used are retrieved (1) from the ground-based network FLAME, located on the flank of the volcano, and (2) from the spaceborne instrument SEVIRI onboard the geostationary satellite MSG. Multiple spaceborne observations, in the infrared and ultraviolet bands, are used to evaluate the model results. Overall, the model results match well with the plume location over the period of the eruption showing the good transport of the volcanic plume by the model, which is linked to the use of a realistic estimation of the altitude of injection of the emissions. However, there are some discrepancies in the plume concentrations of SO2 between the two simulations, which are due to the differences between the two emission flux estimations used that are large on some of the days. These differences are linked to uncertainties in the retrieval methods and observations used to derive SO2 volcanic fluxes. We find that the uncertainties in the satellite-retrieved column of SO2 used for the evaluation of the simulations, linked to the instrument sensitivity and/or the retrieval algorithm, are sometimes nearly as large as the differences between the two simulations. This shows a limitation of the use of satellite retrievals of SO2 concentrations to quantitatively validate modeled volcanic plumes. In the paper, we also discuss approaches to improve the simulation of SO2 concentrations in volcanic plumes through model improvements and also via more advanced methods to more effectively use satellite-derived products.

The presence of volcanic clouds in the atmosphere affects air quality, the environment, climate, human health and aviation safety. The importance of the detection and retrieval of volcanic SO2 lies with risk mitigation as well as with the possibility of providing insights into the mechanisms that cause eruptions. Due to their intrinsic characteristics, satellite measurements have become an essential tool for volcanic monitoring. In recent years, several sensors, with different spectral, spatial and temporal resolutions, have been launched into orbit, significantly increasing the effectiveness of the estimation of the various parameters related to the state of volcanic activity. In this work, the SO2 total masses and fluxes were obtained from several satellite sounders—the geostationary (GEO) MSG-SEVIRI and the polar (LEO) Aqua/Terra-MODIS, NPP/NOAA20-VIIRS, Sentinel5p-TROPOMI, MetopA/MetopB-IASI and Aqua-AIRS—and compared to one another. As a test case, the Christmas 2018 Etna eruption was considered. The characteristics of the eruption (tropospheric with low ash content), the large amount of (simultaneously) available data and the different instrument types and SO2 columnar abundance retrieval strategies make this cross-comparison particularly relevant. Results show the higher sensitivity of TROPOMI and IASI and a general good agreement between the SO2 total masses and fluxes obtained from all the satellite instruments. The differences found are either related to inherent instrumental sensitivity or the assumed and/or calculated SO2 cloud height considered as input for the satellite retrievals. Results indicate also that, despite their low revisit time, the LEO sensors are able to provide information on SO2 flux over large time intervals. Finally, a complete error assessment on SO2 flux retrievals using SEVIRI data was realized by considering uncertainties in wind speed and SO2 abundance.

Multi-sensor strategies are key to the real-time determination of eruptive source parameters (ESPs) of explosive eruptions necessary to forecast accurately both tephra dispersal and deposition. To explore the capacity of these strategies in various eruptive conditions, we analyze data acquired by two Doppler radars, ground- and satellite-based infrared sensors, one infrasound array, visible video-monitoring cameras as well as data from tephra-fallout deposits associated with a weak and a strong paroxysmal event at Mount Etna (Italy). We find that the different sensors provide complementary observations that should be critically analyzed and combined to provide comprehensive estimates of ESPs. First, all measurements of plume height agree during the strong paroxysmal activity considered, whereas some discrepancies are found for the weak paroxysm due to rapid plume and cloud dilution. Second, the event duration, key to convert the total erupted mass (TEM) in the mass eruption rate (MER) and vice versa, varies depending on the sensor used, providing information on different phases of the paroxysm (i.e., unsteady lava fountaining, lava fountain-fed tephra plume, waning phase associated with plume and cloud expansion in the atmosphere). As a result, TEM and MER derived from different sensors also correspond to the different phases of the paroxysms. Finally, satellite retrievals for grain-size can be combined with radar data to provide a first approximation of total grain-size distribution (TGSD) in near real-time. Such a TGSD shows a promising agreement with the TGSD derived from the combination of satellite data and whole deposit grain-size distribution (WDGSD).

Volcanic emissions are a well-known hazard that can have serious impacts on local populations and aviation operations. Whereas several remote sensing observations detect high-intensity explosive eruptions, few studies focus on low intensity and long-lasting volcanic emissions. In this work, we have managed to fully characterize those events by analyzing the volcanic plume produced on the last day of the 2018 Christmas eruption at Mt. Etna, in Italy. We combined data from a visible calibrated camera, a multi-wavelength elastic/Raman Lidar system, from SEVIRI (EUMETSAT-MSG) and MODIS (NASA-Terra/Aqua) satellites and, for the first time, data from an automatic sun-photometer of the aerosol robotic network (AERONET). Results show that the volcanic plume height, ranging between 4.5 and 6 km at the source, decreased by about 0.5 km after 25 km. Moreover, the volcanic plume was detectable by the satellites up to a distance of about 400 km and contained very fine particles with a mean effective radius of about 7 µm. In some time intervals, volcanic ash mass concentration values were around the aviation safety thresholds of 2 × 10−3 g m−3. Of note, Lidar observations show two main stratifications of about 0.25 km, which were not observed at the volcanic source. The presence of the double stratification could have important implications on satellite retrievals, which usually consider only one plume layer. This work gives new details on the main features of volcanic plumes produced during low intensity and long-lasting volcanic plume emissions.

In recent years, some volcanic eruptions have focused scientists’ attention on the detection and monitoring of volcanic clouds, as their impact on the air traffic control system has been unprecedented. In 2010, the Eyjafjallajökull eruption forced the disruption of the airspace of several countries, generating one of the largest air traffic shutdowns ever. Extreme convective events cause many deaths and injuries, and much damage to property every year, accounting for major economic damages related to natural disasters in several countries. Due to global warming, Atlantic tropical cyclones have increased their maximum intensity, hurricanes have more often become extratropical cyclones affecting northern Europe, and southeastern Europe is characterized by increasing annual stormy days. Convective and Volcanic Clouds (CVC) are very dangerous for aviation operations, as they can affect aircraft safety and economic, political, and cultural activities. The detection, nowcasting, and monitoring of CVC is therefore vital for organizing efficient early warning systems.

On the morning of 24 December 2018, an eruptive event occurred at Etna, which was followed the next day by a strong sequence of shallow earthquakes. The eruptive episode lasted until 30 December, ranging from moderate strombolian to lava fountain activity coupled with vigorous ash/gas emissions and a lava flow effusion toward the eastern volcano flank of Valle del Bove. In this work, the data collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instruments on board the Meteosat Second Generation (MSG) geostationary satellite are used to characterize the Etna activity by estimating the proximal and distal eruption parameters in near real time. The inversion of data indicates the onset of eruption on 24 December at 11:15 UTC, a maximum Time Average Discharge Rate (TADR) of 8.3 m3/s, a cumulative lava volume emitted of 0.5 Mm3, and a Volcanic Plume Top Height (VPTH) that reached a maximum altitude of 8 km above sea level (asl). The volcanic cloud ash and SO2 result totally collocated, with an ash amount generally lower than SO2 except on 24 December during the climax phase. A total amount of about 100 and 35 kt of SO2 and ash respectively was emitted during the entire eruptive period, while the SO2 fluxes reached peaks of more than 600 kg/s, with a mean value of about 185 kg/s. The SEVIRI VPTH, ash/SO2 masses, and flux time series have been compared with the results obtained from the ground-based visible (VIS) cameras and FLux Automatic MEasurements (FLAME) networks, and the satellite images collected by the MODerate resolution Imaging Spectroradiometer (MODIS) instruments on board the Terra and Aqua- polar satellites. The analysis indicates good agreement between SEVIRI, VIS camera, and MODIS retrievals with VPTH, ash, and SO2 estimations all within measurement errors. The SEVIRI and FLAME SO2 flux retrievals show significant discrepancies due to the presence of volcanic ash and a gap of data on the FLAME network. The results obtained in this study show the ability of geostationary satellite systems to characterize eruptive events from the source to the atmosphere in near real time during the day and night, thus offering a powerful tool to mitigate volcanic risk on both local population and airspace and to give insight on volcanic processes.

Accurate tracking and forecasting of ash dispersal in the atmosphere and quantification of its uncertainty are of fundamental importance for volcanic risk mitigation. Numerical models and satellite sensors offer two complementary ways to monitor ash clouds in real time, but limits and uncertainties affect both techniques. Numerical forecasts of volcanic clouds can be improved by assimilating satellite observations of atmospheric ash mass load. In this paper, we present a data assimilation procedure aimed at improving the monitoring and forecasting of volcanic ash clouds produced by explosive eruptions. In particular, we applied the Local Ensemble Transform Kalman Filter (LETKF) to the results of the Volcanic Ash Transport and Dispersion model HYSPLIT. To properly simulate the release and atmospheric transport of volcanic ash particles, HYSPLIT has been initialized with the results of the eruptive column model PLUME-MoM. The assimilation procedure has been tested against SEVIRI measurements of the volcanic cloud produced during the explosive eruption occurred at Mt. Etna on 24 December 2018. The results show how the assimilation procedure significantly improves the representation of the current ash dispersal and its forecast. In addition, the numerical tests show that the use of the sequential Ensemble Kalman Filter does not require a precise initialization of the numerical model, being able to improve the forecasts as the assimilation cycles are performed.

In this study, we present a method for extracting the volcanic cloud top height (VCTH) as a plume elevation model (PEM) from orthorectified Landsat 8 data (Level 1). A similar methodology was previously applied to raw Landsat-8 data (Level 0). But level 0 data are not the standard product provided by the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS). Level 0 data are available only on demand and consist on 14 data stripes multiplied by the number of multispectral bands. The standard product for Landsat 8 is the ortho image, available free of charge for end-users. Therefore, there is the need to adapt our previous methodology to Level 1 Landsat data. The advantages of using the standard Landsat products instead of raw data mainly include the fast -ready to use- availability of the data and free access to registered users, which is of major importance during volcanic crises. In this study, we adapt the PEM methodology to the standard Landsat-8 products, with the aim of simplifying the procedure for routine monitoring, offering an opportunity to produce PEM maps. In this study, we present the method. Our approach is applied to the 26 October 2013 Mt. Etna episodes comparing results independent VCTH measures from the spinning enhanced visible and infrared imager (SEVIRI) and the moderate resolution imaging spectroradiometer (MODIS).

The high temporal resolution of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument aboard Meteosat Second Generation (MSG) provides the opportunity to investigate eruptive processes and discriminate different styles of volcanic activity. To this goal, a new detection method based on the wavelet transform of SEVIRI infrared data is proposed. A statistical analysis is performed on wavelet smoothed data derived from SEVIRI Mid-Infrared( MIR) radiances collected from 2011 to 2017 on Mt Etna (Italy) volcano. Time-series analysis of the kurtosis of the radiance distribution allows for reliable hot-spot detection and precise timing of the start and end of eruptive events. Combined kurtosis and gradient trends allow for discrimination of the different activity styles of the volcano, from effusive lava flow, through Strombolian explosions, to paroxysmal fountaining. The same data also allow for the prediction, at the onset of an eruption, of what will be its dominant eruptive style at later stages. The results obtained have been validated against ground-based and literature data.

The brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), is an invasive pest that has expanded its range outside of its original confinements in Eastern Asia, spreading through the United States, Canada and most of the European and Eurasian countries. The invasiveness of this agricultural and public nuisance pest is facilitated by the availability of an array of suitable hosts, an r-selected life history and the release from natural enemies in the invaded zones. Traditional monitoring methods are usually impeded by the lack of time and resources to sufficiently cover large geographical ranges. Therefore, the citizen science initiative “BugMap” was conceived to complement and assist researchers in breaking down the behavior of this invasive pest via a user-friendly, freely available mobile application. The collected data were employed to forecast its predicted distribution and to identify the areas at risk in Trentino, Northern Italy. Moreover, they permitted the uncovering of the seasonal invasion dynamics of this insect, besides providing insight into its phenological patterns, life cycle and potential management methods. Hence, the outcomes of this work emphasize the need to further integrate citizens in scientific endeavors to resolve ecological complications and reduce the gap between the public and science.

From 2011 to 2015, 49 lava fountains occurred at Etna volcano. In this work, the measurements carried out from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board the Meteosat Second Generation (MSG) geostationary satellite, are processed to realize a proximal monitoring of the eruptive activity for each event. The SEVIRI measurements are managed to provide the time series of start and duration of eruption and fountains, Time Averaged Discharge Rate (TADR) and Volcanic Plume Top Height (VPTH). Due to its temperature responsivity, the eruptions start and duration, fountains start and duration and TADR are realized by exploiting the SEVIRI 3.9 μm channel, while the VPTH is carried out by applying a simplified procedure based on the SEVIRI 10.8 μm brightness temperature computation. For each event, the start, duration and TADR have been compared with ground-based observations. The VPTH time series is compared with the results obtained from a procedures-based on the volcanic cloud center of mass tracking in combination with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back-trajectories. The results indicate that SEVIRI is generally able to detect the start of the lava emission few hours before the ground measurements. A good agreement is found for both the start and the duration of the fountains and the VPTH with mean differences of about 1 h, 50 min and 1 km respectively.

This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjalla - jökull event, and equal to 74% for the Grimsvötn event.

Mt. Etna volcano in Italy is one of the most active degassing volcanoes worldwide, emitting a mean of 1.7 Mt/year of Sulphur Dioxide (SO2) in quiescent periods. In this work, SO2 measurements retrieved by Moderate Resolution Imaging Spectroradiometer (MODIS), hyper-spectral Infrared Atmospheric Sounding Interferometer (IASI) and the second Global Ozone Monitoring Experiment (GOME-2) data are compared with the ground-based data from the FLux Automatic MEasurement monitoring network (FLAME). Among the eighteen lava fountain episodes occurring at Mt. Etna in 2011, the 10 April paroxysmal event has been selected as a case-study for the simultaneous observation of the SO2 cloud by satellite and ground-based sensors. For each data-set two retrieval techniques were adopted and the measurements of SO2 mass and flux with their respective uncertainty were obtained. With respect to the FLAME SO2 mass of 4.5 Gg, MODIS, IASI and GOME-2 differ by about 10%, 15% and 30%, respectively. The SO2 flux correlation coefficient between MODIS and FLAME is 0.84. All the retrievals within the respective errors are in agreement with the ground-based measurements supporting the validity of these space measurements.

The session ‘Atmospheric Emissions from Volcanoes’ formed part of the 2014 General Assembly of the Europe-an Geosciences Union (EGU), held in Vienna from 27 April to 2 May. This special issue presents some of the work that was discussed during the session. [...]

In this work the volcanic ash and SO2 retrievals obtained by applying three different procedures (LUT - Look Up Table, NN - Neural Network and VPR - Volcanic Plume Removal) on MODIS Thermal InfraRed (TIR) synthetic measurements have been compared. The synthetic measurements are generated using MODTRAN Radiative Transfer Model (RTM) for defined volcanic cloud configurations. The results, presented as the percentage difference between the retrieved ash and SO2 total masses and the true values used for the synthetic data generation, indicate maximum differences of +/- 15% and +/- 10% for all the procedures and for ash and SO2 retrievals respectively. A sensitivity analysis has been also realized to investigate the influence of volcanic cloud altitude and water vapour profile on SO2 retrievals at 7.3 and 8.6 μm. Results confirm the high sensitivity of the 7.3 μm retrieval to the volcanic cloud altitude and show that the SO2 total masses estimated at 7.3 and 8.6 μm separately can be used to improve the information on the plume height. Finally, the water vapour profile is used to compute the minimum altitude over which the 7.3 μm retrieval is effective.

In this paper, we use calibrated images collected by the video-surveillance system of the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, to retrieve the height of the eruption column during the recent Etna explosive activity. The analysis is carried out on nineteen lava fountains from the New South East Crater dataset. The novel procedure described in this work is achieved in three main steps: at first we calibrated the camera, then we selected the images which recorded the maximum phase of the eruptive activity, and finally we applied an appropriate correction to account for the plume projection on the camera line of sight due to the wind. The results show that the column altitudes range between 6 and 9 km (upper limit of the camera system). The comparison with the plume height values estimated from the analysis of several SEVIRI and MODIS satellite images, show a good agreement. Finally, for nine events we also evaluated the thickness of the volcanic plumes in the umbrella region which ranges between 2 and 3 km.

The characterization of volcanic ash clouds released into the atmosphere during explosive eruptions includes cloud height as a fundamental physical parameter. A novel application is proposed of a method based on parallax data acquired from two geostationary instruments for estimating ash cloud-top height (ACTH). An improved version of the method with a detailed discussion of height retrieval accuracy was applied to estimate ACTH from two datasets acquired by two satellites in favorable positions to fully exploit the parallax effect. A combination of MSG SEVIRI (HRV band; 1000 m nadir spatial resolution, 5 min temporal resolution) and Meteosat-7 MVIRI (VIS band, 2500 m nadir spatial resolution, 30 min temporal resolution) was implemented. Since MVIRI does not acquire data at exactly the same time as SEVIRI, a correction procedure enables compensation for wind advection in the atmosphere. The method was applied to the Mt. Etna, Sicily, Italy, eruption of 23 November 2013. The height of the volcanic cloud was tracked with a top height of ~8.5 km. The ash cloud estimate was applied to the visible channels to show the potential accuracy that will soon be achievable also in the infrared range using the next generation of multispectral imagers. The new constellation of geostationary meteorological satellites will enable full exploitation of this technique for continuous global ACTH monitoring.

Volcanic activity is observed worldwide with a variety of ground and space-based remote sensing instruments, each with advantages and drawbacks. No single system can give a comprehensive description of eruptive activity, and so, a multi-sensor approach is required. This work integrates infrared and microwave volcanic ash retrievals obtained from the geostationary Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI), the polar-orbiting Aqua-MODIS and ground-based weather radar. The expected outcomes are improvements in satellite volcanic ash cloud retrieval (altitude, mass, aerosol optical depth and effective radius), the generation of new satellite products (ash concentration and particle number density in the thermal infrared) and better characterization of volcanic eruptions (plume altitude, total ash mass erupted and particle number density from thermal infrared to microwave). This approach is the core of the multi-platform volcanic ash cloud estimation procedure being developed within the European FP7-APhoRISM project. The Mt. Etna (Sicily, Italy) volcano lava fountaining event of 23 November 2013 was considered as a test case. The results of the integration show the presence of two volcanic cloud layers at different altitudes. The improvement of the volcanic ash cloud altitude leads to a mean difference between the SEVIRI ash mass estimations, before and after the integration, of about the 30%. Moreover, the percentage of the airborne “fine” ash retrieved from the satellite is estimated to be about 1%–2% of the total ash emitted during the eruption. Finally, all of the estimated parameters (volcanic ash cloud altitude, thickness and total mass) were also validated with ground-based visible camera measurements, HYSPLIT forward trajectories, Infrared Atmospheric Sounding Interferometer (IASI) satellite data and tephra deposits.