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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ftdepositonce:oai:depositonce.tu-berlin.de:11303/22293 2024-09-09T19:26:49+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 2024-08-15T13:44:49Z application/pdf https://depositonce.tu-berlin.de/handle/11303/22293 https://doi.org/10.14279/depositonce-21094 en eng https://depositonce.tu-berlin.de/handle/11303/22293 https://doi.org/10.14279/depositonce-21094 2072-4292 https://creativecommons.org/licenses/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 Article publishedVersion 2024 ftdepositonce https://doi.org/10.14279/depositonce-21094 2024-08-19T23:36:38Z 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 accuracy of 92.74% for data containing noise, while the CNN method has a monthly average accuracy of only 77.31% for noisy data. A comparative analysis between the LLE-SVM approach and the convolutional neural network (CNN) method demonstrated the superior anti-interference capabilities of the former. Additionally, the impact of the sea ice melting period on detection accuracy was analyzed. Article in Journal/Newspaper Arctic Arctic Ocean Sea ice TU Berlin: Deposit Once Arctic Arctic Ocean |
institution |
Open Polar |
collection |
TU Berlin: Deposit Once |
op_collection_id |
ftdepositonce |
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 accuracy of 92.74% for data containing noise, while the CNN method has a monthly average accuracy of only 77.31% for noisy data. A comparative analysis between the LLE-SVM approach and the convolutional neural network (CNN) method demonstrated the superior anti-interference capabilities of the former. Additionally, the impact of the sea ice melting period on detection accuracy was analyzed. |
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 |
publishDate |
2024 |
url |
https://depositonce.tu-berlin.de/handle/11303/22293 https://doi.org/10.14279/depositonce-21094 |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Arctic Arctic Ocean Sea ice |
genre_facet |
Arctic Arctic Ocean Sea ice |
op_relation |
https://depositonce.tu-berlin.de/handle/11303/22293 https://doi.org/10.14279/depositonce-21094 2072-4292 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.14279/depositonce-21094 |
_version_ |
1809896375602642944 |