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Dimensionality reduction of optical data: application to total ozone column retrieval

del Aguila, Ana and Molina García, Víctor and Efremenko, Dmitry (2019) Dimensionality reduction of optical data: application to total ozone column retrieval. In: Proceedings of the 2019 conference on Big Data from Space (BiDS'19), pp. 261-264. European Union. Big Data from Space (BiDS’19), 19.-21. Feb. 2019, Munich, Germany. doi: 10.2760/848593. ISBN 978-92-76-00034-1. ISSN 1831-9424.

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Official URL: http://publications.jrc.ec.europa.eu/repository/bitstream/JRC115761/procbids19.pdf


The new generation of atmospheric composition sensors such as TROPOMI, deliver a great amount of data, which is recognized as Big Data. To process the challenging data volumes of spectral information, fast radiative transfer models (RTMs) are required. However, the bottleneck in remote sensing retrieval problems is the computation of the radiative transfer. Thus, the operational processing of remote sensing data requires high-performance RTMs for simulating spectral radiances (level-1 data). In particular, ozone total column retrieval algorithms use the level-1 data in the Huggins band (325-335 nm). Hence, accurate simulation of this absorption band may require several hundreds of monochromatic computations. However, hyper-spectral input data for RTMs has a redundant information, which can be excluded by using the dimensionality reduction techniques. In addition, there is a strong correlation between the input optical data for RTMs and output radiances. Such statistical dependency can be taken into account for accelerating level-1 data simulations using principal component analysis (PCA), thereby providing the performance enhancement for the whole processing chain. In this paper we analyze the efficiency and potential benefits of the optical data dimensionality reduction schemes for simulating the Huggins band and discuss several modifications of this approach.

Item URL in elib:https://elib.dlr.de/126638/
Document Type:Conference or Workshop Item (Poster)
Title:Dimensionality reduction of optical data: application to total ozone column retrieval
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
del Aguila, AnaAna.delAguilaPerez (at) dlr.deUNSPECIFIED
Molina García, Víctorvictor.molinagarcia (at) dlr.deUNSPECIFIED
Efremenko, Dmitrydmitry.efremenko (at) dlr.deUNSPECIFIED
Journal or Publication Title:Proceedings of the 2019 conference on Big Data from Space (BiDS'19)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.2760/848593
Page Range:pp. 261-264
EditorsEmailEditor's ORCID iD
Publisher:European Union
Keywords:PCA, data-driven algorithms, trace gas retrieval
Event Title:Big Data from Space (BiDS’19)
Event Location:Munich, Germany
Event Type:international Conference
Event Dates:19.-21. Feb. 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Optical remote sensing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Efremenko, Dr Dmitry
Deposited On:25 Feb 2019 10:44
Last Modified:31 Jul 2019 20:24

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