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

• Published in: Remote Sensing
• 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: Multidisciplinary Digital Publishing Institute (MDPI) 2021
• Subjects: Institut für Solar-Terrestrische Physik; Arctic; Climate change; Sea ice
• Online Access: https://elib.dlr.de/145661/; https://elib.dlr.de/145661/1/2021-ZhuY-001.pdf; https://www.mdpi.com/2072-4292/13/22/4577

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.