Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
نویسندگان
چکیده
The knowledge of Arctic Sea ice coverage is particular importance in studies climate change. This study develops a new sea 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 (FYI), and multi-year (MYI). A total eight extracted observables five months are applied to classify OW, FYI, MYI using ML random forest (RF) support vector (SVM) two-step strategy. Firstly, randomly selected 30% samples whole dataset used as training set build for discriminating OW ice. performance evaluated remaining 70% validating with type Special Sensor Microwave Imager Sounder (SSMIS) provided by Ocean Ice Satellite Application Facility (OSISAF). overall accuracy RF SVM 98.83% 98.60% respectively distinguishing Then, ice, including FYI MYI, split into test dataset. input variables train FYI-MYI classifiers, which achieve an 84.82% 71.71% classifiers. Finally, every month testing turn cross-validate proposed classifier. results indicate strong sensitivity GNSS signals types great potential applications.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2021
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs13224577