Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

Two effective machine learning-aided sea ice monitoring methods are investigated using 42 months of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data collected by the TechDemoSat-1 (TDS-1). The two-dimensional delay waveforms with different Doppler spread characteristics are...

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Bibliographic Details
Main Authors: Yongchao Zhu, Tingye Tao, Kegen Yu, Xiaochuan Qu, Shuiping Li, Jens Wickert, Maximilian Semmling
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Remote Sensing
Subjects:
Delay-Doppler Map (DDM)
Global Navigation Satellite System-Reflectometry (GNSS-R)
decision tree
random forest
sea ice monitoring
Online Access:https://www.mdpi.com/2072-4292/12/22/3751

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https://www.mdpi.com/2072-4292/12/22/3751

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