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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Bibliographic Details
Main Authors: Hu, Yuan, Hua, Xifan, Yan, Qingyun, Liu, Wei, Jiang, Zhihao, Wickert, Jens
Format: Article in Journal/Newspaper
Language:English
Published: 2024
Subjects:
LLE
Online Access:https://depositonce.tu-berlin.de/handle/11303/22293
https://doi.org/10.14279/depositonce-21094
id ftdepositonce:oai:depositonce.tu-berlin.de:11303/22293
record_format openpolar
spelling 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