Multisensor data fusion of operational sea ice observations
Multisensor data fusion (MDF) is a process/technique of combining observations from multiple sensors to provide a more robust, accurate and complete description of the concerned object, environment or process. In this paper we introduce a new MDF method, multisensor optimal data fusion (MODF), to fu...
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Online Access: | http://dx.doi.org/10.3389/fmars.2024.1366002 https://www.frontiersin.org/articles/10.3389/fmars.2024.1366002/full |
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crfrontiers:10.3389/fmars.2024.1366002 2024-05-12T08:10:43+00:00 Multisensor data fusion of operational sea ice observations Wang, Keguang Wang, Caixin Dinessen, Frode Spreen, Gunnar Ricker, Robert Tian-Kunze, Xiangshan 2024 http://dx.doi.org/10.3389/fmars.2024.1366002 https://www.frontiersin.org/articles/10.3389/fmars.2024.1366002/full unknown Frontiers Media SA https://creativecommons.org/licenses/by/4.0/ Frontiers in Marine Science volume 11 ISSN 2296-7745 Ocean Engineering Water Science and Technology Aquatic Science Global and Planetary Change Oceanography journal-article 2024 crfrontiers https://doi.org/10.3389/fmars.2024.1366002 2024-04-18T07:56:53Z Multisensor data fusion (MDF) is a process/technique of combining observations from multiple sensors to provide a more robust, accurate and complete description of the concerned object, environment or process. In this paper we introduce a new MDF method, multisensor optimal data fusion (MODF), to fuse different operational sea ice observations around Svalbard. The overall MODF includes regridding, univariate multisensor optimal data merging (MODM), multivariate check of consistency, and generation of new variables. For MODF of operational sea ice observations around Svalbard, the AMSR2 sea ice concentration (SIC) is firstly merged with the Norwegian Meteorological Institute ice chart. Then the daily SMOS sea ice thickness (SIT) is merged with the weekly CS2SMOS SIT to form a daily CS2SMOS SIT, which is further refined to be consistent with the SIC through consistency check. Finally sea ice volume (SIV) and its uncertainty are calculated based on the merged SIC and fused SIT. The fused products provide an improved, united, consistent and multifaceted description for the operational sea ice observations, they also provide consistent descriptions of sea ice edge and marginal ice zone. We note that uncertainties may vary during the regridding process, and therefore correct determination of the observation uncertainties is critically important for MDF. This study provides a basic framework for managing multivariate multisensor observations. Article in Journal/Newspaper Sea ice Svalbard Frontiers (Publisher) Svalbard Frontiers in Marine Science 11 |
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Open Polar |
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topic |
Ocean Engineering Water Science and Technology Aquatic Science Global and Planetary Change Oceanography |
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Ocean Engineering Water Science and Technology Aquatic Science Global and Planetary Change Oceanography Wang, Keguang Wang, Caixin Dinessen, Frode Spreen, Gunnar Ricker, Robert Tian-Kunze, Xiangshan Multisensor data fusion of operational sea ice observations |
topic_facet |
Ocean Engineering Water Science and Technology Aquatic Science Global and Planetary Change Oceanography |
description |
Multisensor data fusion (MDF) is a process/technique of combining observations from multiple sensors to provide a more robust, accurate and complete description of the concerned object, environment or process. In this paper we introduce a new MDF method, multisensor optimal data fusion (MODF), to fuse different operational sea ice observations around Svalbard. The overall MODF includes regridding, univariate multisensor optimal data merging (MODM), multivariate check of consistency, and generation of new variables. For MODF of operational sea ice observations around Svalbard, the AMSR2 sea ice concentration (SIC) is firstly merged with the Norwegian Meteorological Institute ice chart. Then the daily SMOS sea ice thickness (SIT) is merged with the weekly CS2SMOS SIT to form a daily CS2SMOS SIT, which is further refined to be consistent with the SIC through consistency check. Finally sea ice volume (SIV) and its uncertainty are calculated based on the merged SIC and fused SIT. The fused products provide an improved, united, consistent and multifaceted description for the operational sea ice observations, they also provide consistent descriptions of sea ice edge and marginal ice zone. We note that uncertainties may vary during the regridding process, and therefore correct determination of the observation uncertainties is critically important for MDF. This study provides a basic framework for managing multivariate multisensor observations. |
format |
Article in Journal/Newspaper |
author |
Wang, Keguang Wang, Caixin Dinessen, Frode Spreen, Gunnar Ricker, Robert Tian-Kunze, Xiangshan |
author_facet |
Wang, Keguang Wang, Caixin Dinessen, Frode Spreen, Gunnar Ricker, Robert Tian-Kunze, Xiangshan |
author_sort |
Wang, Keguang |
title |
Multisensor data fusion of operational sea ice observations |
title_short |
Multisensor data fusion of operational sea ice observations |
title_full |
Multisensor data fusion of operational sea ice observations |
title_fullStr |
Multisensor data fusion of operational sea ice observations |
title_full_unstemmed |
Multisensor data fusion of operational sea ice observations |
title_sort |
multisensor data fusion of operational sea ice observations |
publisher |
Frontiers Media SA |
publishDate |
2024 |
url |
http://dx.doi.org/10.3389/fmars.2024.1366002 https://www.frontiersin.org/articles/10.3389/fmars.2024.1366002/full |
geographic |
Svalbard |
geographic_facet |
Svalbard |
genre |
Sea ice Svalbard |
genre_facet |
Sea ice Svalbard |
op_source |
Frontiers in Marine Science volume 11 ISSN 2296-7745 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3389/fmars.2024.1366002 |
container_title |
Frontiers in Marine Science |
container_volume |
11 |
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1798854209541505024 |