Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition

Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. H...

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Autores: Wang, Sheng, Baum, Andreas, Zarco-Tejada, Pablo J., Dam-Hansen, Carsten, Thorseth, Anders, Bauer-Gottwein, Peter, Bandini, Filippo, García, Mónica
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/206983
Acceso en línea:http://hdl.handle.net/10261/206983
Access Level:acceso abierto
Palabra clave:Reflectance
Sensor calibration
Cloud shadow removal
Tucker tensor decomposition
Unmanned Aerial System
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spelling Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decompositionWang, ShengBaum, AndreasZarco-Tejada, Pablo J.Dam-Hansen, CarstenThorseth, AndersBauer-Gottwein, PeterBandini, FilippoGarcía, MónicaReflectanceSensor calibrationCloud shadow removalTucker tensor decompositionUnmanned Aerial SystemUnlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55oN latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3%. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes.The authors would like to thank the EU and Innovation Fund Denmark (IFD) for funding, in the frame of the collaborative international consortium AgWIT financed under the ERA-NET Co-fund Water Works 2015 Call. This ERA-NET is an integral part of the 2016 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI). This study was also supported by the Smart UAV project from IFD [125-2013-5]. SW acknowledge an internal PhD grant from the Department of Environmental Engineering at DTU and a short-term research stage with PZT financed by the COST action OPTIMISE.Peer reviewedElsevierInnovation Fund DenmarkEuropean CommissionTechnical University of DenmarkConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202019info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/206983reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1016/j.isprsjprs.2019.06.017Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2069832026-05-22T06:33:51Z
dc.title.none.fl_str_mv Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition
title Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition
spellingShingle Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition
Wang, Sheng
Reflectance
Sensor calibration
Cloud shadow removal
Tucker tensor decomposition
Unmanned Aerial System
title_short Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition
title_full Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition
title_fullStr Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition
title_full_unstemmed Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition
title_sort Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition
dc.creator.none.fl_str_mv Wang, Sheng
Baum, Andreas
Zarco-Tejada, Pablo J.
Dam-Hansen, Carsten
Thorseth, Anders
Bauer-Gottwein, Peter
Bandini, Filippo
García, Mónica
author Wang, Sheng
author_facet Wang, Sheng
Baum, Andreas
Zarco-Tejada, Pablo J.
Dam-Hansen, Carsten
Thorseth, Anders
Bauer-Gottwein, Peter
Bandini, Filippo
García, Mónica
author_role author
author2 Baum, Andreas
Zarco-Tejada, Pablo J.
Dam-Hansen, Carsten
Thorseth, Anders
Bauer-Gottwein, Peter
Bandini, Filippo
García, Mónica
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Innovation Fund Denmark
European Commission
Technical University of Denmark
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Reflectance
Sensor calibration
Cloud shadow removal
Tucker tensor decomposition
Unmanned Aerial System
topic Reflectance
Sensor calibration
Cloud shadow removal
Tucker tensor decomposition
Unmanned Aerial System
description Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55oN latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3%. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/206983
url http://hdl.handle.net/10261/206983
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1016/j.isprsjprs.2019.06.017
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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