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DecSolNet: A noise resistant missing information recovery framework for daily satellite NO2 columns

Zhu, Songyan and Xu, Jian and Yu, Chao and Wang, Yapeng and Efremenko, Dmitry S. and Li, Xiaoying and Sui, Zhengwei (2021) DecSolNet: A noise resistant missing information recovery framework for daily satellite NO2 columns. Atmospheric Environment, 246, p. 118143. Elsevier. doi: 10.1016/j.atmosenv.2020.118143. ISSN 1352-2310.

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Official URL: https://www.sciencedirect.com/science/article/pii/S1352231020308736

Abstract

A novel statistical method (hereafter referred to as DecSolNet) for reconstructing satellite NO2 columns is introduced. The method has been developed and evaluated by comparing its performance with four benchmark models in three scenarios. When the amount of satellite data is limited, DecSolNet outperforms the benchmark models and its performance does not degrade with noisy inputs. The implementation of DecSolNet consists of: (1) feature extraction, sequential data decomposition in both temporal and frequency domains; (2) NO2 columns reconstruction by training a deep neural network. In three cross-validations, the averaged R2 score of DecSolNet reaches 0.9, which is better than that of the most benchmark models. The multi-layer perceptron (MLP) has a higher R2 score, but it degrades greatly with noisy inputs, while the performance of DecSolNet remains robust with an R2 of ̃0.8. The bias of DecSolNet is small with an average of 1.61 μg/m3. In addition, DecSolNet is a reliable learning machine, the averaged loss and standard deviation are 0.42 μg/m3 and 0.04 μg/m3, respectively.

Item URL in elib:https://elib.dlr.de/140386/
Document Type:Article
Title:DecSolNet: A noise resistant missing information recovery framework for daily satellite NO2 columns
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Zhu, SongyanDepartment of Geography, University of ExeterUNSPECIFIED
Xu, Jianjian.xu (at) dlr.dehttps://orcid.org/0000-0003-2348-125X
Yu, ChaoAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaUNSPECIFIED
Wang, YapengKey Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological AdministrationUNSPECIFIED
Efremenko, Dmitry S.dmitry.efremenko (at) dlr.dehttps://orcid.org/0000-0002-7449-5072
Li, XiaoyingAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaUNSPECIFIED
Sui, ZhengweiChina Centre for Resources Satellite Data and Application, Beijing, ChinaUNSPECIFIED
Date:1 February 2021
Journal or Publication Title:Atmospheric Environment
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:246
DOI :10.1016/j.atmosenv.2020.118143
Page Range:p. 118143
Editors:
EditorsEmailEditor's ORCID iD
Schauer, James J.UNSPECIFIEDUNSPECIFIED
Publisher:Elsevier
ISSN:1352-2310
Status:Published
Keywords:NO2 columns, Remote sensing, Data reconstruction, Time series decomposition, EMD, Deep learning
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 - Project Climatic relevance of atmospheric tracer gases, aerosols and clouds
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Xu, Dr.-Ing. Jian
Deposited On:14 Jan 2021 10:09
Last Modified:14 Jan 2021 10:09

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