A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic)

Bibliographic Details
Published in:IEEE Transactions on Geoscience and Remote Sensing
Main Authors: Asim, Muhammad, Matsuoka, Atsushi, Ellingsen, Pal Gunnar, Brekke, Camilla, Eltoft, Torbjorn, Blix, Katalin
Other Authors: The Nansen Legacy [Research Council of Norway (RCN)], Center for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA), National Aeronautics and Space Administration (NASA) Research Opportunities in Space and Earth Sciences (ROSES) Project, Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-Climate
Format: Article in Journal/Newspaper
Language:unknown
Published: Institute of Electrical and Electronics Engineers (IEEE) 2023
Subjects:
Arctic
Barents Sea
Online Access:http://dx.doi.org/10.1109/tgrs.2022.3228393
http://xplorestaging.ieee.org/ielx7/36/10006360/09980426.pdf?arnumber=9980426
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spelling crieeecr:10.1109/tgrs.2022.3228393 2024-06-23T07:49:34+00:00 A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic) Asim, Muhammad Matsuoka, Atsushi Ellingsen, Pal Gunnar Brekke, Camilla Eltoft, Torbjorn Blix, Katalin The Nansen Legacy [Research Council of Norway (RCN)] Center for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA) National Aeronautics and Space Administration (NASA) Research Opportunities in Space and Earth Sciences (ROSES) Project Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-Climate 2023 http://dx.doi.org/10.1109/tgrs.2022.3228393 http://xplorestaging.ieee.org/ielx7/36/10006360/09980426.pdf?arnumber=9980426 unknown Institute of Electrical and Electronics Engineers (IEEE) https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 IEEE Transactions on Geoscience and Remote Sensing volume 61, page 1-19 ISSN 0196-2892 1558-0644 journal-article 2023 crieeecr https://doi.org/10.1109/tgrs.2022.3228393 2024-06-13T04:11:52Z Article in Journal/Newspaper Arctic Barents Sea IEEE Publications Arctic Barents Sea IEEE Transactions on Geoscience and Remote Sensing 61 1 19
institution Open Polar
collection IEEE Publications
op_collection_id crieeecr
language unknown
author2 The Nansen Legacy [Research Council of Norway (RCN)]
Center for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA)
National Aeronautics and Space Administration (NASA) Research Opportunities in Space and Earth Sciences (ROSES) Project
Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-Climate
format Article in Journal/Newspaper
author Asim, Muhammad
Matsuoka, Atsushi
Ellingsen, Pal Gunnar
Brekke, Camilla
Eltoft, Torbjorn
Blix, Katalin
spellingShingle Asim, Muhammad
Matsuoka, Atsushi
Ellingsen, Pal Gunnar
Brekke, Camilla
Eltoft, Torbjorn
Blix, Katalin
A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic)
author_facet Asim, Muhammad
Matsuoka, Atsushi
Ellingsen, Pal Gunnar
Brekke, Camilla
Eltoft, Torbjorn
Blix, Katalin
author_sort Asim, Muhammad
title A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic)
title_short A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic)
title_full A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic)
title_fullStr A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic)
title_full_unstemmed A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic)
title_sort new spectral harmonization algorithm for landsat-8 and sentinel-2 remote sensing reflectance products using machine learning: a case study for the barents sea (european arctic)
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2023
url http://dx.doi.org/10.1109/tgrs.2022.3228393
http://xplorestaging.ieee.org/ielx7/36/10006360/09980426.pdf?arnumber=9980426
geographic Arctic
Barents Sea
geographic_facet Arctic
Barents Sea
genre Arctic
Barents Sea
genre_facet Arctic
Barents Sea
op_source IEEE Transactions on Geoscience and Remote Sensing
volume 61, page 1-19
ISSN 0196-2892 1558-0644
op_rights https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
https://doi.org/10.15223/policy-029
https://doi.org/10.15223/policy-037
op_doi https://doi.org/10.1109/tgrs.2022.3228393
container_title IEEE Transactions on Geoscience and Remote Sensing
container_volume 61
container_start_page 1
op_container_end_page 19
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