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Volcanic SO2 plume height retrieval from UV sensors using a full-physics inverse learning machine algorithm

Efremenko, Dmitry and Loyola, Diego and Hedelt, Pascal and Spurr, Robert (2017) Volcanic SO2 plume height retrieval from UV sensors using a full-physics inverse learning machine algorithm. International Journal of Remote Sensing, 38 (50), pp. 1-27. Taylor & Francis. DOI: 10.1080/01431161.2017.1348644 ISSN 0143-1161

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Official URL: http://www.tandfonline.com/doi/full/10.1080/01431161.2017.1348644

Abstract

Precise knowledge of the location and height of the volcanic sulphur dioxide (SO2) plume is essential for accurate determination of SO2 emitted by volcanic eruptions. Current SO2 plume height retrieval algorithms based on ultraviolet (UV) satellite measurements are very time-consuming and therefore not suitable for near-real-time applications. In this work we present a novel method called the full-physics inverse learning machine (FP-ILM) algorithm for extremely fast and accurate retrieval of the SO2 plume height. FP-ILM creates a mapping between the spectral radiance and the geophysical parameters of interest using supervised learning methods. The FP-ILM combines smart sampling methods, dimensionality reduction techniques, and various linear and non-linear regression analysis schemes based on principal component analysis and neural networks. The computationally expensive operations in FP-ILM are the radiative transfer model computations of a training dataset and the determination of the inversion operator - these operations are performed off-line. The application of the resulting inversion operator to real measurements is extremely fast since it is based on calculations of simple regression functions. Retrieval of the SO2 plume height is demonstrated for the volcanic eruptions of Mt. Kasatochi (in 2008) and Eyjafjallajökull (in 2010), measured by the GOME-2 (Global Ozone Monitoring Instrument - 2) UV instrument on-board MetOp-A.

Item URL in elib:https://elib.dlr.de/113789/
Document Type:Article
Title:Volcanic SO2 plume height retrieval from UV sensors using a full-physics inverse learning machine algorithm
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Efremenko, Dmitrydmitry.efremenko (at) dlr.deUNSPECIFIED
Loyola, Diegodiego.loyola (at) dlr.deUNSPECIFIED
Hedelt, Pascalpascal.hedelt (at) dlr.deUNSPECIFIED
Spurr, Robertrt solutionsUNSPECIFIED
Date:22 August 2017
Journal or Publication Title:International Journal of Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:38
DOI :10.1080/01431161.2017.1348644
Page Range:pp. 1-27
Publisher:Taylor & Francis
ISSN:0143-1161
Status:Published
Keywords:plume height retrieval; machine learning; regularization; dimensionality reduction
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Atmosphären- und Klimaforschung
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Efremenko, Dr Dmitry
Deposited On:25 Aug 2017 11:32
Last Modified:23 Feb 2019 00:21

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