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

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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https://doaj.org/article/5d11d8932e054122aa37734b61c7d9f1

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