Articles | Volume 7, issue 12
https://doi.org/10.5194/amt-7-4023-2014
© Author(s) 2014. 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-7-4023-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
01 Dec 2014
Research article |
| 01 Dec 2014
A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO2 using MODIS data
A. Piscini
CORRESPONDING AUTHOR
Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
M. Picchiani
Earth Observation Laboratory, D.I.C.I.I. – University of Tor Vergata, Rome, Italy
M. Chini
Centre de Recherche Public – Gabriel Lippmann, Belvaux, Luxembourg
S. Corradini
Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
L. Merucci
Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
F. Del Frate
Earth Observation Laboratory, D.I.C.I.I. – University of Tor Vergata, Rome, Italy
S. Stramondo
Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
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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
- Monitoring Earth's atmosphere with Sentinel-5 TROPOMI and Artificial Intelligence: Quantifying volcanic SO2 emissions C. Corradino et al. 10.1016/j.rse.2024.114463
- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
- Plume Tracker: Interactive mapping of volcanic sulfur dioxide emissions with high-performance radiative transfer modeling V. Realmuto & A. Berk 10.1016/j.jvolgeores.2016.07.001
- Insights into geological processes with CO2 remote sensing – A review of technology and applications M. Queißer et al. 10.1016/j.earscirev.2018.11.016
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- VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model L. Bugliaro et al. 10.5194/nhess-22-1029-2022
- Determination of complex refractive indices and optical properties of volcanic ashes in the thermal infrared based on generic petrological compositions D. Piontek et al. 10.1016/j.jvolgeores.2021.107174
- Retrieving Volcanic Ash Top Height through Combined Polar Orbit Active and Geostationary Passive Remote Sensing Data W. Zhu et al. 10.3390/rs12060953
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
- A new machine-learning-based analysis for improving satellite-retrieved atmospheric composition data: OMI SO2 as an example C. Li et al. 10.5194/amt-15-5497-2022
- Nonlinear Spectral Unmixing for the Characterisation of Volcanic Surface Deposit and Airborne Plumes from Remote Sensing Imagery G. Licciardi et al. 10.3390/geosciences7030046
- Comparison of Multi-Temporal Differential Interferometry Techniques Applied to the Measurement of Bucharest City Subsidence M. Gheorghe & I. Armaş 10.1016/j.proenv.2016.03.027
- Simultaneous retrieval of volcanic sulphur dioxide and plume height from hyperspectral data using artificial neural networks A. Piscini et al. 10.1093/gji/ggu152
- Fast Dst computation by applying deep learning to Swarm satellite magnetic data G. Cianchini et al. 10.1016/j.asr.2021.10.051
14 citations as recorded by crossref.
- An ensemble of state-of-the-art ash dispersion models: towards probabilistic forecasts to increase the resilience of air traffic against volcanic eruptions M. Plu et al. 10.5194/nhess-21-2973-2021
- Characterization of Volcanic Cloud Components Using Machine Learning Techniques and SEVIRI Infrared Images F. Torrisi et al. 10.3390/s22207712
- Automatic volcanic ash detection from MODIS observations using a back-propagation neural network T. Gray & R. Bennartz 10.5194/amt-8-5089-2015
- Monitoring Earth's atmosphere with Sentinel-5 TROPOMI and Artificial Intelligence: Quantifying volcanic SO2 emissions C. Corradino et al. 10.1016/j.rse.2024.114463
- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
- Plume Tracker: Interactive mapping of volcanic sulfur dioxide emissions with high-performance radiative transfer modeling V. Realmuto & A. Berk 10.1016/j.jvolgeores.2016.07.001
- Insights into geological processes with CO2 remote sensing – A review of technology and applications M. Queißer et al. 10.1016/j.earscirev.2018.11.016
- Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System S. Valade et al. 10.3390/rs11131528
- VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model L. Bugliaro et al. 10.5194/nhess-22-1029-2022
- Determination of complex refractive indices and optical properties of volcanic ashes in the thermal infrared based on generic petrological compositions D. Piontek et al. 10.1016/j.jvolgeores.2021.107174
- Retrieving Volcanic Ash Top Height through Combined Polar Orbit Active and Geostationary Passive Remote Sensing Data W. Zhu et al. 10.3390/rs12060953
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
- A new machine-learning-based analysis for improving satellite-retrieved atmospheric composition data: OMI SO2 as an example C. Li et al. 10.5194/amt-15-5497-2022
- Nonlinear Spectral Unmixing for the Characterisation of Volcanic Surface Deposit and Airborne Plumes from Remote Sensing Imagery G. Licciardi et al. 10.3390/geosciences7030046
3 citations as recorded by crossref.
- Comparison of Multi-Temporal Differential Interferometry Techniques Applied to the Measurement of Bucharest City Subsidence M. Gheorghe & I. Armaş 10.1016/j.proenv.2016.03.027
- Simultaneous retrieval of volcanic sulphur dioxide and plume height from hyperspectral data using artificial neural networks A. Piscini et al. 10.1093/gji/ggu152
- Fast Dst computation by applying deep learning to Swarm satellite magnetic data G. Cianchini et al. 10.1016/j.asr.2021.10.051
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