# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
###### Abstract  
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. GNSS-R; Delay-Doppler Map; machine learning; sea ice classification; TDS-1 2021 11 1223 12 surface of FYI is usually smoother than that of MYI. In addition, the salinity of FYI is higher than that of MYI. The FYI around the floe edges tends to undergo deformation when it collides with thicker ice. In general, the ice that survives at least one summer is regarded as MYI, which retains low salinity values and an undulating surface. These characteristics of different sea ice types are the basis for classification. A wide variety of techniques has been applied to characterize changes in sea ice. Sea ice can be monitored from different platforms, such as buoys [4], ships [5], aircraft [6], and satellites [7]. Among them, satellite-based microwave remote sensing has been regarded as the most effective tool for monitoring sea ice [7]. In recent years, Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) has emerged as a powerful tool for sensing bio-geophysical features using L-band signals scattered from the Earth's surface [8]. GNSS-R was initially proposed for ocean altimetry in 1993 [9] after the concept of GNSS-R was proposed in 1988 [10]. Subsequently, the scope of the applications of GNSS-R has been extended to various fields, such as wind speed retrieval [11], snow depth estimation [12], soil moisture sensing [13], ocean altimetry [14], and sea ice detection [15]. Most GNSS-R studies were carried out using reflected L-band data collected on ground-based, airborne, and spaceborne platforms, and the latter one is regarded as the future trend due to its global coverage and high mobility [16]. The United-Kingdom (UK) TechDemoSat-1 [16] and NASA Cyclone GNSS (CYGNSS) [17], launched in 2014 and 2016 respectively, have promoted the research of spaceborne GNSS-R since their data are publicly available. Particularly, the TDS-1 data can be used for polar research as its global coverage, while the CYGNSS can only be applied in middle and lower latitude regions as its coverage of interest is the oceans within the latitude of \(\pm 38^{\circ}\). Recently, some other spaceborne GNSS-R missions have been successfully carried out one after another, such as the Chinese BuFeng-1 A/B [18], and Fengyun 3E [19].