The successful launch of TechDemoSat-1 (TDS-1) and CYGNSS (Cyclone Global Navigation Satellite System) in 2014 and 2016, respectively, has made the study of spaceborne GNSS-R highly noticed [19; 20]. A large variety of spaceborne GNSS-R datasets collected in these two missions have become available to the public; especially the data from TDS-1 cover the Arctic and Antarctic regions with high density, which provides the opportunity of monitoring sea ice using spaceborne GNSS-R. In addition, the two Chinese satellites called BuFeng-1 A/B, which are part of the first Chinese GNSS-R mission, were launched on 5 June 2019 [21]. The datasets of TDS-1 have been exploited in the studies of monitoring sea ice over the past few years. The delay-Doppler Map (DDM) is one of the most important observables of spaceborne GNSS-R mission; the DDM of sea water shows more spreading than that of sea ice. The monitoring of sea ice using TDS-1 DDM was firstly illustrated in [22], where the number of DDM pixels with power above a certain threshold was selected as the criteria to distinguish sea ice from water. The pixel number-based method was further expanded to the differential DDM, whose pixel number and power summation were used to identify four transitions, including ice-water, water-water, water-ice and ice-ice [23]. Another method of sea ice transition recognition was exploited through analyzing the radar image reconstructed by applying the deconvolution algorithm to DDMs [24]. Through the analysis of DDM, the two-dimensional delay waveform corresponding to different Doppler shifts were extracted to sense sea ice in [25], where the relationship between received waveforms and the theoretical waveform of a flat surface was estimated. Recent studies [25; 26; 27; 28] indicated that sea ice can be correctly discriminated from water in up to 98.22% of cases in the monitoring of sea ice compared to collocated passive microwave data. The applications of TDS-1 data for sea ice altimetry were explored in a number of previous studies [29; 30; 31], while the raw data used in [29] and [30] are not in the standard dataset open to the public. Besides the detection of sea ice, GNSS-R has been applied to retrieve sea ice parameters, such as sea ice type [32], sea ice concentration [33] and sea ice thickness [34]. With the development of Artificial Intelligence (AI), Machine Learning (ML)-based approaches have been widely employed to the geosciences and engineering problems [35; 36; 37]. AI is a broader concept than ML, which addresses the use of computers to mimic the cognitive functions of humans. ML is a subset of AI and focuses on the ability of machines to receive a set of data and learn for themselves, changing algorithms as they learn more about the information they are processing. As one of the most important subdivisions of AI, ML has been proven effective for applications in many parts of remote sensing, such as image classification, object detection and some retrieval problems. In recent years, ML has been successfully exploited to the applications of monitoring sea ice through analyzing various remote sensing data. The application of ML for monitoring sea ice using TDS-1 data was initially demonstrated in [38], where a neural network method was applied to detect sea ice. Hereafter, sea ice concentration was estimated through interpreting DDMs using the convolutional neural network (CNN) [39]. The ML-based sea ice monitoring method was further exploited in [40], where the support vector machine (SVM) was utilized to obtain better performance. Although three ML-based approaches show great potential in sensing sea ice, they can still be further improved. Moreover, only the original DDM and values extracted from DDM were used as input parameters in these studies. Using features,which depict the characteristics of DDMs, as input elements may enhance monitoring performance and data processing efficiency. The applications of ML may be categorized into three aspects [35]: classification, developing empirical model and improving computation efficiency.