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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ftdoajarticles:oai:doaj.org/article:435a3403b38f40a3b44456430e017590 2024-09-09T19:26:35+00:00 Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding Yuan Hu Xifan Hua Qingyun Yan Wei Liu Zhihao Jiang Jens Wickert 2024-07-01T00:00:00Z https://doi.org/10.3390/rs16142621 https://doaj.org/article/435a3403b38f40a3b44456430e017590 EN eng MDPI AG https://www.mdpi.com/2072-4292/16/14/2621 https://doaj.org/toc/2072-4292 doi:10.3390/rs16142621 2072-4292 https://doaj.org/article/435a3403b38f40a3b44456430e017590 Remote Sensing, Vol 16, Iss 14, p 2621 (2024) delay-Doppler maps (DDMs) Global Navigation Satellite System-Reflectometry (GNSS-R) local linear embedding (LLE) sea ice detection Science Q article 2024 ftdoajarticles https://doi.org/10.3390/rs16142621 2024-08-05T17:48:50Z 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 Directory of Open Access Journals: DOAJ Articles Arctic Arctic Ocean Remote Sensing 16 14 2621 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
delay-Doppler maps (DDMs) Global Navigation Satellite System-Reflectometry (GNSS-R) local linear embedding (LLE) sea ice detection Science Q |
spellingShingle |
delay-Doppler maps (DDMs) Global Navigation Satellite System-Reflectometry (GNSS-R) local linear embedding (LLE) sea ice detection Science Q Yuan Hu Xifan Hua Qingyun Yan Wei Liu Zhihao Jiang Jens Wickert Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding |
topic_facet |
delay-Doppler maps (DDMs) Global Navigation Satellite System-Reflectometry (GNSS-R) local linear embedding (LLE) sea ice detection Science Q |
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 |
Yuan Hu Xifan Hua Qingyun Yan Wei Liu Zhihao Jiang Jens Wickert |
author_facet |
Yuan Hu Xifan Hua Qingyun Yan Wei Liu Zhihao Jiang Jens Wickert |
author_sort |
Yuan Hu |
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 |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/rs16142621 https://doaj.org/article/435a3403b38f40a3b44456430e017590 |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Arctic Arctic Ocean Sea ice |
genre_facet |
Arctic Arctic Ocean Sea ice |
op_source |
Remote Sensing, Vol 16, Iss 14, p 2621 (2024) |
op_relation |
https://www.mdpi.com/2072-4292/16/14/2621 https://doaj.org/toc/2072-4292 doi:10.3390/rs16142621 2072-4292 https://doaj.org/article/435a3403b38f40a3b44456430e017590 |
op_doi |
https://doi.org/10.3390/rs16142621 |
container_title |
Remote Sensing |
container_volume |
16 |
container_issue |
14 |
container_start_page |
2621 |
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1809896175202992128 |