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Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands

del Águila, Ana and Efremenko, Dmitry S. and Molina García, Víctor and Kataev, M.Yu (2020) Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands. Remote Sensing, 12 (8), pp. 1-19. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs12081250 ISSN 2072-4292

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Official URL: https://www.mdpi.com/2072-4292/12/8/1250

Abstract

Current atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models based on the clustering of the spectral radiances computed with a low-stream RTM and the regression analysis performed for the low-stream and multi-stream RTMs within each cluster. This approach, which we refer to as the Cluster Low-Streams Regression (CLSR) method, is applied for computing the radiance spectra in the O2 A-band at 760 nm and the CO2 band at 1610 nm for five atmospheric scenarios. The CLSR method is also compared with the principal component analysis (PCA)-based RTM, showing an improvement in terms of accuracy and computational performance over PCA-based RTMs. As low-stream models, the two-stream and the single-scattering RTMs are considered. We show that the error of this approach is modulated by the optical thickness of the atmosphere. Nevertheless, the CLSR method provides a performance enhancement of almost two orders of magnitude compared to the LBL model, while the error of the technique is below 0.1% for both bands.

Item URL in elib:https://elib.dlr.de/134698/
Document Type:Article
Title:Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
del Águila, AnaAna.delAguilaPerez (at) dlr.dehttps://orcid.org/0000-0001-9006-9631
Efremenko, Dmitry S.Dmitry.Efremenko (at) dlr.dehttps://orcid.org/0000-0002-7449-5072
Molina García, VíctorVictor.MolinaGarcia (at) dlr.dehttps://orcid.org/0000-0002-2564-5396
Kataev, M.YuTomsk State University of Control Systems and Radioelectronics (TUSUR)UNSPECIFIED
Date:10 April 2020
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:12
DOI :10.3390/rs12081250
Page Range:pp. 1-19
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:hyperspectral data; fast radiative transfer models; acceleration techniques; regression; O2 A-band; CO2 band; GOSAT; TROPOMI
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):Vorhaben Spectroscopic Methods in Remote Sensing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: del Aguila Perez, Ana
Deposited On:22 Apr 2020 12:03
Last Modified:18 May 2020 17:49

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