Articles | Volume 4, issue 12
Atmos. Meas. Tech., 4, 2619–2631, 2011
https://doi.org/10.5194/amt-4-2619-2011
Atmos. Meas. Tech., 4, 2619–2631, 2011
https://doi.org/10.5194/amt-4-2619-2011

Research article 07 Dec 2011

Research article | 07 Dec 2011

Volcanic ash detection and retrievals using MODIS data by means of neural networks

M. Picchiani1, M. Chini2, S. Corradini2, L. Merucci2, P. Sellitto3, F. Del Frate1, and S. Stramondo2 M. Picchiani et al.
  • 1Dipartimento di Informatica Sistemi e Produzione – Tor Vergata University, Rome, Italy
  • 2Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
  • 3Laboratoire Inter-universitaire des Systèmes Atmosphériques (LISA), UMR7583, Universités Paris-Est et Paris Diderot, CNRS, Créteil, France

Abstract. Volcanic ash clouds detection and retrieval represent a key issue for aviation safety due to the harming effects on aircraft. A lesson learned from the recent Eyjafjallajokull eruption is the need to obtain accurate and reliable retrievals on a real time basis.

In this work we have developed a fast and accurate Neural Network (NN) approach to detect and retrieve volcanic ash cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Thermal InfraRed (TIR) spectral range. Some measurements collected during the 2001, 2002 and 2006 Mt. Etna volcano eruptions have been considered as test cases.

The ash detection and retrievals obtained from the Brightness Temperature Difference (BTD) algorithm are used as training for the NN procedure that consists in two separate steps: ash detection and ash mass retrieval. The ash detection is reduced to a classification problem by identifying two classes: "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. A segmentation procedure has also been tested to remove the false ash pixels detection induced by the presence of high meteorological clouds. The segmentation procedure shows a clear advantage in terms of classification accuracy: the main drawback is the loss of information on ash clouds distal part.

The results obtained are very encouraging; indeed the ash detection accuracy is greater than 90%, while a mean RMSE equal to 0.365 t km−2 has been obtained for the ash mass retrieval. Moreover, the NN quickness in results delivering makes the procedure extremely attractive in all the cases when the rapid response time of the system is a mandatory requirement.

Download