Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding ...
Sea ice plays a critical role in the Earth’s climate system, and its variations affect ecosystem stability. This study introduces a novel method for detecting sea ice in the Arctic Ocean using bidirectional radar reflections from the Global Navigation Satellite System (GNSS). Utilizing delay-Doppler...
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Technische Universität Berlin
2024
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Online Access: | https://dx.doi.org/10.14279/depositonce-21094 https://depositonce.tu-berlin.de/handle/11303/22293 |
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ftdatacite:10.14279/depositonce-21094 2024-09-15T17:53:48+00:00 Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding ... Hu, Yuan Hua, Xifan Yan, Qingyun Liu, Wei Jiang, Zhihao Wickert, Jens 2024 https://dx.doi.org/10.14279/depositonce-21094 https://depositonce.tu-berlin.de/handle/11303/22293 en eng Technische Universität Berlin Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::620 Ingenieurwissenschaften und zugeordnete Tätigkeiten delay-Doppler maps Global Navigation Satellite System-Reflectometry local linear embedding sea ice detection DDMs LLE GNSS-R JournalArticle Article ScholarlyArticle article-journal 2024 ftdatacite https://doi.org/10.14279/depositonce-21094 2024-09-02T08:32:03Z Sea ice plays a critical role in the Earth’s climate system, and its variations affect ecosystem stability. This study introduces a novel method for detecting sea ice in the Arctic Ocean using bidirectional radar reflections from the Global Navigation Satellite System (GNSS). Utilizing delay-Doppler maps (DDM) from the UK TechDemoSat-1 (TDS-1) satellite mission and surface data from the U.S. National Oceanic and Atmospheric Administration (NOAA), we employ the local linear embedding (LLE) algorithm for feature extraction. This approach notably reduces training costs and enhances real-time performance, while maintaining a high accuracy and robust noise immunity level. Focusing on the region above 70° north latitude throughout 2018, we aimed to distinguish between sea ice and seawater. The extracted DDM features via LLE are input into a support vector machine (SVM) for classification. The results indicate that our method achieves an accuracy of over 99% for selected low-noise data and a monthly average ... Article in Journal/Newspaper Arctic Ocean Sea ice DataCite |
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language |
English |
topic |
600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::620 Ingenieurwissenschaften und zugeordnete Tätigkeiten delay-Doppler maps Global Navigation Satellite System-Reflectometry local linear embedding sea ice detection DDMs LLE GNSS-R |
spellingShingle |
600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::620 Ingenieurwissenschaften und zugeordnete Tätigkeiten delay-Doppler maps Global Navigation Satellite System-Reflectometry local linear embedding sea ice detection DDMs LLE GNSS-R Hu, Yuan Hua, Xifan Yan, Qingyun Liu, Wei Jiang, Zhihao Wickert, Jens Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding ... |
topic_facet |
600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::620 Ingenieurwissenschaften und zugeordnete Tätigkeiten delay-Doppler maps Global Navigation Satellite System-Reflectometry local linear embedding sea ice detection DDMs LLE GNSS-R |
description |
Sea ice plays a critical role in the Earth’s climate system, and its variations affect ecosystem stability. This study introduces a novel method for detecting sea ice in the Arctic Ocean using bidirectional radar reflections from the Global Navigation Satellite System (GNSS). Utilizing delay-Doppler maps (DDM) from the UK TechDemoSat-1 (TDS-1) satellite mission and surface data from the U.S. National Oceanic and Atmospheric Administration (NOAA), we employ the local linear embedding (LLE) algorithm for feature extraction. This approach notably reduces training costs and enhances real-time performance, while maintaining a high accuracy and robust noise immunity level. Focusing on the region above 70° north latitude throughout 2018, we aimed to distinguish between sea ice and seawater. The extracted DDM features via LLE are input into a support vector machine (SVM) for classification. The results indicate that our method achieves an accuracy of over 99% for selected low-noise data and a monthly average ... |
format |
Article in Journal/Newspaper |
author |
Hu, Yuan Hua, Xifan Yan, Qingyun Liu, Wei Jiang, Zhihao Wickert, Jens |
author_facet |
Hu, Yuan Hua, Xifan Yan, Qingyun Liu, Wei Jiang, Zhihao Wickert, Jens |
author_sort |
Hu, Yuan |
title |
Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding ... |
title_short |
Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding ... |
title_full |
Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding ... |
title_fullStr |
Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding ... |
title_full_unstemmed |
Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding ... |
title_sort |
sea ice detection from gnss-r data based on local linear embedding ... |
publisher |
Technische Universität Berlin |
publishDate |
2024 |
url |
https://dx.doi.org/10.14279/depositonce-21094 https://depositonce.tu-berlin.de/handle/11303/22293 |
genre |
Arctic Ocean Sea ice |
genre_facet |
Arctic Ocean Sea ice |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.14279/depositonce-21094 |
_version_ |
1810429861030789120 |