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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Bibliographic Details
Main Authors: Zhu, Yongchao, Tao, Tingye, Li, Jiangyang, Yu, Kegen, Wang, Lei, Qu, Xiaochuan, Li, Shuiping, Semmling, Maximilian, Wickert, Jens
Format: Article in Journal/Newspaper
Language:English
Published: Technische Universität Berlin 2021
Subjects:
620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
GNSS-R
Delay-Doppler Map
machine learning
sea ice classification
TDS-1
Arctic
Climate change
Sea ice
Online Access:https://dx.doi.org/10.14279/depositonce-12822
https://depositonce.tu-berlin.de/handle/11303/14049
Description
Summary: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 ...

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