# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
###### Abstract
Many applications of GNSS-R make use of the specular scattering geometry since GNSS signals reflected from the Earth's surface have the greatest amplitude at specular scattering. The Delay-Doppler Map (DDM), which is one of the most important GNSS-R observables, demonstrates the power scattering from the reflected surface as a function of time delays and Doppler shifts (Figure 1). The reflection over rough surfaces, such as open water (OW), is usually regarded as incoherent, which results in a \"horseshoe\" shape of DDM presented in Figure 1a. The specular scattering is considered coherent when the reflected surface is relatively smooth. The typical coherent DDM is shown in Figure 1b. Figure 1: Typical TDS-1 Delay-Doppler Maps (DDMs) of (**a**) incoherent and (**b**) coherent returns. The incoherent and coherent DDMs are observed over open water (OW) (\(8.05^{\circ}\)E, \(79.53^{\circ}\)N) and first-year ice (FYI) (\(0.93^{\circ}\)W, \(79.99^{\circ}\)N), respectively, on 26 November 2015. The TDS-1 data have been successfully used to sense sea ice parameters over the past few years. [PERSON] et al. [20] firstly explored the sensitivity of TDS-1 Delay-Doppler Map (DDM) to sea ice presence using the pixel number of DDM with a signal-to-noise ratio (SNR) above a threshold. Similarly, [PERSON] et al. [21] proposed a differential DDM observable, which was used to recognize ice-water, water-water, water-ice, and ice-ice transitions. [PERSON] et al. [22] reconstructed a radar image based on DDM to distinguish sea ice from water. [PERSON] et al. [15] applied a matched filter ice detection approach with a probability of detection of 98.5%, a probability of false alarm of 3.6%, and a probability of error of 2.5%. Afterward, another study has proven that spaceborne GNSS-R can be effective for sea ice discrimination with a success rate of up to 98.22% compared to collocated passive microwave sea ice data [23]. [PERSON] et al. [24] combined two features extracted from DDMs to detect sea ice with an agreement of 98.4% and 96.6% in the Antarctic and Arctic regions by comparing with the European Space Agency Climate Change Initiative sea ice concentration product. On the other hand, TDS-1 data were expanded to altimetry applications [25; 26]. The ice sheet melt was also investigated in [27] using TDS-1 data. Furthermore, it has been shown that spaceborne GNSS-R is also useful for retrieving sea ice thickness [28], sea ice concentration [29], and sea ice type [30]. The GNSS-R observables derived from DDMs were used to classify Arctic sea ice in [30], where the TDS-1 sea ice types were compared with the sea ice type maps derived from Synthetic Aperture Radar (SAR) measurements. In recent years, machine learning (ML) based methods have been widely used in geosciences and remote sensing applications [31]. ML has been proven powerful for applications in various remote sensing fields, such as classification [32; 33], object detection [34], and parameter estimates [35]. [PERSON] et al. [36] firstly adopted the neural network (NN) method to detect sea ice using spaceborne GNSS-R DDMs from TDS-1. This study demonstrated the potential of an NN-based approach for sea ice detection and sea ice concentration (SIC) estimation, which was further explored through the convolutional neural network (CNN) algorithm [37]. The DDMs were directly used as input variables in the CNN-based approach. Compared to NN and CNN, support vector machine (SVM) achieved the best performance in sea ice detection [38]. These three studies utilized the original DDM and the values derived from DDMs as input features for sensing sea ice. [PERSON] et al. [39] employed feature sequences that depict the characteristics of DDMs as input parameters to monitor sea ice using the decision tree (DT) and random forest (RF) algorithms.