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...

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

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