In this work the classification of Sentinel-3 Sea and Land Surface Temperature (S3-SLSTR) images with a focus on volcanic cloud detection through a Neural Networks (NNs) approach is presented. Since the hazardous nature of eruptions, a fast and reliable method to monitor the evolution of volcanic clouds in real time is of primary interest. NNs represent a suitable tool for this purpose given their short processing time once trained, and their ability to solve complex problems as those related to natural events. The present research starts from the generation of the training patterns by means of MODerate resolution Imaging Spectroradiometer (MODIS) data collected during the 2010 Eyjafjallajokull (Iceland) eruption, it goes through the training of the NN, and ends with the application of the NN-based model to SLSTR data collected during the 2019 Raikoke (Kuril Island, Russia) eruption.

In this study we present a novel approach dedicated to the retrieval of volcanic ash parameters by means of data acquired from the Sea and Land Surface Temperature Radiometer on board of the Copernicus Sentinel-3. In this framework, we developed a procedure combining Radiative Transfer Model simulations and Neural Network for estimating three volcanic ash parameters such as aerosol optical depth, effective radius and ash mass. The Radiative Transfer Model simulations have been considered for producing synthetic training sets, which have been used in the training phase of the Neural Networks development. In particular, nine latitude's belts have been identified for training several Neural Networks ensuring the global coverage of the method. The approach has been tested by comparing the results of the trained NN with the ones obtained by applied the state-of-art Look Up Table and the Volcanic Plume Retrieval procedures. The results of the methodologies applied on Raikoke, 2019 eruption demonstrated the feasibility of the proposed approach by registering values of the correlation coefficient between all the three methods ranging between the 65% and the 94%.

The rapidly evolving field of open science has made scientists agree on the idea that data, workflows and services, should be findable, accessible, interoperable, and thus optimally reusable. These principles apply to the geohazard community also, dealing with natural phenomena such as volcanic and seismic activity. However, there is still a weakness regarding research sharing and re-use through the scientific community, due to lack of technological solutions and their long-term implementation. We present a lifecycle management service ecosystem based on Research Objects to manage scientific works, in terms of methods, workflows and intermediate or final results, according to open science principles. We show a data cube service for scalable structured data discovery, access and interoperation. These services foster the adoption of remote sensing (e.g., Copernicus) data in the geohazard scientific community enabling research lifecycle documentation. We present test cases of ground deformation analysis and modelling, and volcanic cloud mapping.

Satellite-based detection of volcanic eruptions, using infrared radiometric data from Low Earth Orbit (LEO) spectroradiometers, may lead to an ambiguous detection in the proximity of the volcanic vent during sub-Plinian volcanic events. The thermal-infrared (TIR) brightness-temperature difference signatures saturate because of the large tephra particle within the expanding plume. In this respect, the use LEO spaceborne millimeter-wave (MMW) radiometric observations can help since plumes at millimeter wavelength are less optically opaque than at micron ones. To demonstrate this synergy, we show the analysis of the 2014 Kelud eruption case study considering LEO measurements and detection algorithms based on TIR and MMW.

In recent years many studies concerning the monitoring of volcanic activity have been carried out to develop ever more accurate and refine methods which allow to face the emergencies related to an eruption event. In our work we present different approaches for the volcanic ash cloud detection and retrieval using Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) data. As test case the SLSTR image collected on Raikoke volcano the 22 June 2019 at 00:07 UTC has been considered. A neural network based algorithm able to detect and distinguish volcanic and meteorological clouds, and the underlying surfaces, has been implemented and compared with two consolidated approaches: the RGB (Red-Green-Blue) and the Brightness Temperature Difference procedures. For the ash retrieval parameters (aerosol optical depth, effective radius and ash mass), three different methods have been compared: the reliable and consolidated LUTp(Look Up Table) procedure, the very fast VPR (Volcanic Plume Retrieval) algorithm and a neural network based model.

During a volcanic eruption, large quantities of Sulphur dioxide (SO2) are sometimes emitted into the atmosphere. Rapid detection and tracking ofvolcanic SO2 clouds might be beneficial to air traffic security and to predict any correlated impact on the environment; for example, the possibility of acid rain events. Within the presented work, we exploited Sentinel-5p radiance data (Level 1 b) to detect and retrieve SO2 volcanic emissions through a neural network based algorithmthat produces rapid SO2 vertical column estimates. The dataset used for training the net was composed of 13 TROPOMI Level 2 “Offline” SO2 data collected during the Etna Volcano eruption that occurred in 2018 from 22 December to 1 January. Experimental results are very encouraging and open to the perspective ofmake available a new and stable product for monitoring atmospheric SO2 clouds on a global scale based on Sentinel-5p acquisitions.

In this work data observed by the geostationary MSG-SEVIRI and the polar NASA-Terra/Aqua-MODIS orbiting satellite instruments, have been used for the proximal and distal monitoring of the 24-30 December 2018 Etna eruption. The combined use of the SEVIRI high repetition time and the MODIS high spatial resolution allows a reliable near real time volcanic characterization from the source to the atmosphere. For the proximal monitoring the parameters estimated are the eruption starts and duration and the volcanic plume top height, while the distal monitoring was inverted relying on the determination of the volcanic cloud altitude and the ash/SO2retrievals. Achieved products were validated by comparing these results with those observed remotely by ground based networks.Results obtained in this study show the ability of satellite-based systems to entirely follow eruptive events in near real time, offering a powerful tool to mitigate volcanic risk on both local population and airspace.

A-train satellite data, acquired during the Calbuco volcano (Chile) sub-Plinian eruption in April 2015, are discussed to explore the complementarity of spaceborne observations in the microwave (MW), thermal infrared (TIR), and visible wavelengths for both near-source plume and distal ash clouds. The analysis shows that TIR-based detection techniques are not suitable near the volcanic vent where rising convective columns are associated with large optical depths. Detection and parametric estimates of near-source tephra mass loading and plume height from MW radiometric data, available 69 min after the eruption onset, are proposed. Results indicate a maximum plume altitude of about 21 km above the sea level and an ash mass of 3.65 × 1010kg, in agreement with mass values obtained from empirical formulas, but less than proximal- distal mass deposit of 1.86 × 1011kg. This discrepancy may be explained by extrapolating Advanced Technology Microwave Sounder-based estimates to 6 h, thus obtaining a total mass of about 1.90 × 1011kg. Distal volcanic cloud retrievals are derived from TIR imagery and results show a good agreement between Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) retrievals of total mass taking into account the overpass time shift. If only the overlapping pixels between MODIS and VIIRS are considered, the respective estimates are 1.90 × 109kg and 1.80 × 109kg. TIR radiometric estimates of distal ash cloud height and mass loadings are also compared with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar retrievals. For low-to-medium optically thick ash cloud, average Cloud-Aerosol Lidar with Orthogonal Polarization-derived mass loading is about 0.8 g/m2against 0.4 g/m2from VIIRS and 1.4 g/m2from MODIS.

Volcanic eruptions affect at different levels the population and economy of interested areas. Moreover, volcanic ash detection represents a key issue for aviation safety due to the harming effects on aircraft. For these reasons, an accurate and fast analysis of the data is needed to monitor the phenomena's evolution and to manage the risk mitigation phase. In this scenario, the introduction of an inversion approach based on Neural Networks (NNs) has significant interest to reduce the need of human interpretation of the ash detection maps as those generated by the application of brightness temperature difference approach. In this work we show that NNs algorithms are suitable for an accurate mapping of ash cloud on Moderate Resolution Imaging Spectroradiometer (MODIS) images in a very cloudy scenario as the ones of 2010 Eyjafjallajökull and 2011 Grimsvötn eruptions.

The possibility offered by space-based sensors represents an irreplaceable resource for monitoring in near real time the eruption activities. The high revisit time of sensor like MODIS, seems to be the most effective way to mitigate the aviation hazard imaging the phenomenon evolution. In this work we propose a neural networks based approach to the volcanic ash mass retrieval. In comparison with the techniques based on radiative transfer models, the proposed algorithm has shown similar accuracy and faster computation. This issue can be of real interest to address the problems inherent the volcanic activity in short time. A set of MODIS images collected during the Eyjafjallajokull eruption, occurred from the 14thof April to the 23rdof May 2010, has been used to analyze the performance variations due to different selection of the algorithm inputs, i.e. the MODIS channels from visible to thermal infrared electromagnetic spectrum. The best wavelength sets for the retrieval of the ash mass, optical thickness and effective radius have been identified by means of neural network pruning algorithm.

The great eruption of the Icelandic Eyjafjallajokull volcano that occurred from the 14thof April to the 23rdof May 2010 injected large and dense ash clouds into the atmosphere, causing major international air traffic disruption worldwide.

The moderate Resolution Imaging Spectroradiometer (MODIS) is a multispectral satellite instrument operating from the visible to thermal infrared spectral range. FALL3D is a 3-D time-dependent Eulerian model for the transport and deposition of volcanic particles. In this letter, quantitative comparison between the volcanic cloud ash mass and optical depth retrieved by MODIS and modeled by FALL3D has been performed. Three MODIS images collected on October 28, 29, and 30 on Mt. Etna volcano during the 2002 eruption have been considered as test cases. The results show a general good agreement between the retrieved and the modeled volcanic clouds in the first 300 km from the vents. Even if the modeled volcanic cloud area is systematically wider than the retrieved area, the ash total mass is comparable and varies between 35 and 60 kt and between 20 and 42 kt for FALL3D and MODIS, respectively. The mean aerosol optical depth (AOD) values are in good agreement and approximately equal to 0.8. When the whole volcanic clouds are considered the ash areas, then the total ash masses, computed by FALL3D model, are significantly greater than the same parameters retrieved from the MODIS data, while the mean AOD values remain in very good agreement and equal to about 0.6. The volcanic cloud direction in its distal part is not coincident for the October 29 and 30, 2002 images due to the difference between the real and the modeled local wind fields. Finally, the MODIS maps show regions of high mass and AOD due to volcanic puffs not modeled by FALL3D.

In this paper, a sensitivity analysis and procedure development for volcanic-plume sulfur dioxide and ash retrievals using ground thermal infrared camera have been carried out. The semiconductor device camera, considered as a reference, has a spectral range of 8-14 ¿m with noise equivalent temperature difference that is better than 100 mK at 300 K. The camera will be used to monitor and assess the hazards of Mt. Etna volcano to mitigate the risk and impact of volcanic eruptions on the civil society and transports. A minimum number of filters have been selected for sulfur dioxide (SO2) and volcanic ash retrievals. The sensitivity study has been carried out to determine the SO2and volcanic ash minimum concentration detectable by the system varying the camera geometry and the atmospheric profiles. Results show a meaningful sensitivity increase considering high instrument altitudes and low camera-elevation angles. For all geometry configurations and monthly profiles, the sensitivity limit varies between 0.5 and 2 g ·m-2for SO2columnar abundance and between 0.02 and 1 for ash optical depth. Two procedures to detect SO2and ash, based on the least square fit method and on the brightness temperature difference (BTD) algorithm, respectively, have also been proposed. Results show that high concentration of atmospheric water vapor columnar content significantly reduces the ash-plume effect on the BTD. A water vapor-correction procedure introduced improves the ash retrievals and the cloud discrimination in every season, considering all the camera geometries.

The ASI-SRV (Sistema Rischio Vulcanico) project is devoted to the development of an integrated system based on EO and Non EO data to respond to specific needs of the Italian Civil Protection Department (DPC). ASI-SRV provides the capability to import many different EO and Non EO data into the system, it maintains a repository where the acquired data have to be stored and generates selected products which will be functional to the different volcanic activity phases. The processing modules for Radar and EO Optical sensors data allow to estimate a number of parameters which include: surface thermal proprieties, gas, aerosol and ash emissions and to characterize the volcanic products in terms of composition and geometry, surface deformations in terms of displacements and velocity. All the generated products are related to Italian actives volcanoes and three test sites have been chosen to demonstrate the capability of this integrated system: Vesuvio, Campi Flegrei (Campania region) and Etna (Sicilia region). In this paper the first results obtained by means of modules developed within the ASI-SRV project and dedicated to the processing of EO historical series are presented.