Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers

The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected o...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yongchao Zhu, Tingye Tao, Jiangyang Li, Kegen Yu, Lei Wang, Xiaochuan Qu, Shuiping Li, Maximilian Semmling, Jens Wickert
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	GNSS-R Delay-Doppler Map machine learning sea ice classification TDS-1 Science Q
Acceso en línea:	https://doaj.org/article/5d11d8932e054122aa37734b61c7d9f1
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5d11d8932e054122aa37734b61c7d9f1
record_format	dspace
spelling	oai:doaj.org-article:5d11d8932e054122aa37734b61c7d9f12021-11-25T18:54:30ZSpaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers10.3390/rs132245772072-4292https://doaj.org/article/5d11d8932e054122aa37734b61c7d9f12021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4577https://doaj.org/toc/2072-4292The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications.Yongchao ZhuTingye TaoJiangyang LiKegen YuLei WangXiaochuan QuShuiping LiMaximilian SemmlingJens WickertMDPI AGarticleGNSS-RDelay-Doppler Mapmachine learningsea ice classificationTDS-1ScienceQENRemote Sensing, Vol 13, Iss 4577, p 4577 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GNSS-R Delay-Doppler Map machine learning sea ice classification TDS-1 Science Q
spellingShingle	GNSS-R Delay-Doppler Map machine learning sea ice classification TDS-1 Science Q Yongchao Zhu Tingye Tao Jiangyang Li Kegen Yu Lei Wang Xiaochuan Qu Shuiping Li Maximilian Semmling Jens Wickert Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
description	The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications.
format	article
author	Yongchao Zhu Tingye Tao Jiangyang Li Kegen Yu Lei Wang Xiaochuan Qu Shuiping Li Maximilian Semmling Jens Wickert
author_facet	Yongchao Zhu Tingye Tao Jiangyang Li Kegen Yu Lei Wang Xiaochuan Qu Shuiping Li Maximilian Semmling Jens Wickert
author_sort	Yongchao Zhu
title	Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
title_short	Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
title_full	Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
title_fullStr	Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
title_full_unstemmed	Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
title_sort	spaceborne gnss-r for sea ice classification using machine learning classifiers
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5d11d8932e054122aa37734b61c7d9f1
work_keys_str_mv	AT yongchaozhu spacebornegnssrforseaiceclassificationusingmachinelearningclassifiers AT tingyetao spacebornegnssrforseaiceclassificationusingmachinelearningclassifiers AT jiangyangli spacebornegnssrforseaiceclassificationusingmachinelearningclassifiers AT kegenyu spacebornegnssrforseaiceclassificationusingmachinelearningclassifiers AT leiwang spacebornegnssrforseaiceclassificationusingmachinelearningclassifiers AT xiaochuanqu spacebornegnssrforseaiceclassificationusingmachinelearningclassifiers AT shuipingli spacebornegnssrforseaiceclassificationusingmachinelearningclassifiers AT maximiliansemmling spacebornegnssrforseaiceclassificationusingmachinelearningclassifiers AT jenswickert spacebornegnssrforseaiceclassificationusingmachinelearningclassifiers
_version_	1718410589994221568

Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers

Ejemplares similares