10 Jun 2022
10 Jun 2022
Status: this preprint is currently under review for the journal AMT.

Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

Ilaria Petracca1, Davide De Santis1, Matteo Picchiani2,3, Stefano Corradini4, Lorenzo Guerrieri4, Fred Prata5, Luca Merucci4, Dario Stelitano4, Fabio Del Frate1, Giorgia Salvucci1, and Giovanni Schiavon1 Ilaria Petracca et al.
  • 1Department of Civil Engineering and Computer Science Engineering, Tor Vergata University of Rome, 00133, Italy
  • 2GEO-K s.r.l., Rome, Italy
  • 3GMATICS s.r.l., Rome, Italy
  • 4Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Rome, Italy
  • 5AIRES Pty Ltd., Australia

Abstract. The accurate automatic volcanic cloud detection by means of satellite data is a challenging task and of great concern for both scientific community and stakeholder due to the well-known issues generated by a strong eruption event in relation to aviation safety and health impact. In this context, machine learning techniques applied to recent spaceborne sensors acquired data have shown promising results in the last years.

This work focuses on the application of a neural network based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. The classification of the clouds and of the other surfaces composing the scene is also carried out. The neural network has been trained with MODIS (MODerate resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallajökull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the events comparable latitudes foster the robustness of the approach, which allows overcoming the lack in SLSTR products collected in previous mid-high latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared with RGB visual inspection and BTD (Brightness Temperature Difference) procedure. Moreover, the comparison between the ash cloud obtained by neural network and a plume mask manually generated for the specific SLSTR considered images, shows significant agreement. Thus, the proposed approach allows an automatic image classification during eruption events, which it is also considerably faster than time-consuming manually algorithms (e.g. find the best BTD product-specific threshold). Furthermore, the whole image classification indicates an overall reliability of the algorithm, in particular for meteo-clouds recognition and discrimination from volcanic clouds.

Finally, the results show that the NN developed for the SLSTR nadir view is able to properly classify also the SLSTR oblique view images.

Ilaria Petracca et al.

Status: open (until 16 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Ilaria Petracca et al.

Ilaria Petracca et al.


Total article views: 216 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
174 36 6 216 2 2
  • HTML: 174
  • PDF: 36
  • XML: 6
  • Total: 216
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 10 Jun 2022)
Cumulative views and downloads (calculated since 10 Jun 2022)

Viewed (geographical distribution)

Total article views: 213 (including HTML, PDF, and XML) Thereof 213 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 02 Jul 2022
Short summary
The authors propose a near real-time procedure for the detection of volcanic cloud by means of satellite data and neural networks. The developed model shows good performances in terms of discrimination of ashy pixels in Sentinel-3/SLSTR images if compared with other approaches which are time consuming, case-specific and not automatic. The proposed algorithm could be significantly helpful for emergency management related to eruption events.