Articles | Volume 4, issue 12
https://doi.org/10.5194/amt-4-2619-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-4-2619-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Volcanic ash detection and retrievals using MODIS data by means of neural networks
M. Picchiani
Dipartimento di Informatica Sistemi e Produzione – Tor Vergata University, Rome, Italy
M. Chini
Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
S. Corradini
Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
L. Merucci
Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
P. Sellitto
Laboratoire Inter-universitaire des Systèmes Atmosphériques (LISA), UMR7583, Universités Paris-Est et Paris Diderot, CNRS, Créteil, France
F. Frate
Dipartimento di Informatica Sistemi e Produzione – Tor Vergata University, Rome, Italy
S. Stramondo
Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
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- Simultaneous retrieval of volcanic sulphur dioxide and plume height from hyperspectral data using artificial neural networks A. Piscini et al. 10.1093/gji/ggu152
- A retrospective analysis of the Shinmoedake (Japan) eruption of 26–27 January 2011 by means of Japanese geostationary satellite data F. Marchese et al. 10.1016/j.jvolgeores.2013.10.011
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- Automatic volcanic ash detection from MODIS observations using a back-propagation neural network T. Gray & R. Bennartz 10.5194/amt-8-5089-2015
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
- Review of Thermal Infrared Applications and Requirements for Future High-Resolution Sensors J. Sobrino et al. 10.1109/TGRS.2015.2509179
- A Novel Vision-Based Classification System for Explosion Phenomena S. Abusaleh et al. 10.3390/jimaging3020014
- Nonlinear Spectral Unmixing for the Characterisation of Volcanic Surface Deposit and Airborne Plumes from Remote Sensing Imagery G. Licciardi et al. 10.3390/geosciences7030046
- The Art of Reading Explosion Phenomena: Science and Algorithms S. Abusaleh et al. 10.1109/ACCESS.2017.2780277
- Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption F. Pardini et al. 10.3390/atmos11040359
- Predicting the hazard area of the volcanic ash caused by Mt. Ontake Eruption S. Lee & C. Lee 10.7780/kjrs.2014.30.6.8
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- Retrieval of dust storm aerosols using an integrated Neural Network model F. Xiao et al. 10.1016/j.cageo.2015.02.016
17 citations as recorded by crossref.
- A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO<sub>2</sub> using MODIS data A. Piscini et al. 10.5194/amt-7-4023-2014
- Evolution of the 2011 Mt. Etna ash and SO2 lava fountain episodes using SEVIRI data and VPR retrieval approach L. Guerrieri et al. 10.1016/j.jvolgeores.2014.12.016
- Simultaneous retrieval of volcanic sulphur dioxide and plume height from hyperspectral data using artificial neural networks A. Piscini et al. 10.1093/gji/ggu152
- A retrospective analysis of the Shinmoedake (Japan) eruption of 26–27 January 2011 by means of Japanese geostationary satellite data F. Marchese et al. 10.1016/j.jvolgeores.2013.10.011
- Satellite retrieval of aerosol microphysical and optical parameters using neural networks: a new methodology applied to the Sahara desert dust peak M. Taylor et al. 10.5194/amt-7-3151-2014
- Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations A. Di Noia et al. 10.5194/amt-8-281-2015
- A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data M. Sist et al. 10.3390/app11104686
- Predicting the extent of the volcanic ash dispersion using GOCI image and HYSPLIT model - A case study of the 17 Sep, 2013 eruption in SAKURAJIMA volcano - S. Lee et al. 10.7780/kjrs.2014.30.2.12
- Retrieving Volcanic Ash Top Height through Combined Polar Orbit Active and Geostationary Passive Remote Sensing Data W. Zhu et al. 10.3390/rs12060953
- Automatic volcanic ash detection from MODIS observations using a back-propagation neural network T. Gray & R. Bennartz 10.5194/amt-8-5089-2015
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
- Review of Thermal Infrared Applications and Requirements for Future High-Resolution Sensors J. Sobrino et al. 10.1109/TGRS.2015.2509179
- A Novel Vision-Based Classification System for Explosion Phenomena S. Abusaleh et al. 10.3390/jimaging3020014
- Nonlinear Spectral Unmixing for the Characterisation of Volcanic Surface Deposit and Airborne Plumes from Remote Sensing Imagery G. Licciardi et al. 10.3390/geosciences7030046
- The Art of Reading Explosion Phenomena: Science and Algorithms S. Abusaleh et al. 10.1109/ACCESS.2017.2780277
- Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption F. Pardini et al. 10.3390/atmos11040359
- Predicting the hazard area of the volcanic ash caused by Mt. Ontake Eruption S. Lee & C. Lee 10.7780/kjrs.2014.30.6.8
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