## 5 Conclusions  
In this study, two machine learning-aided GNSS-R methods have been proposed to monitor sea ice using 42 months of TDS-1 data. The sea ice monitoring results are validated with the SIE data from OSISAF. The results showed that the proposed approach successfully distinguishes sea ice from water. The proposed RF- and DT-based sea ice monitoring approaches achieve an overall accuracy of 98.03% and 97.51%, respectively, in the Arctic regions, and 95.96% and 95.46%, respectively, in the Antarctic regions. Another ML-based method (i.e., SVM) used in the previous study [40] is also applied for  
Figure 11: Examples of sea ice monitoring results validated against ASI SIC (sea ice concentration) maps from AMSR-2 data on the southwest side of Greenland on 14 March 2018 using four different methods: (**a**) the REWD thresholding approach, (**b**) the REWI thresholding approach, (**c**) the DT-based method in this study and (**d**) the RF-based method in this study. The land and sea water are represented as light brown and white, respectively. The sea ice concentration (SIC) is demonstrated by the color bar. The green and blue points represent the detected sea ice and sea water, respectively, while the red points represent the false detection. comparison in this study. The SVM-based method achieves an overall accuracy of 97.62% and 95.61%, respectively, in the Arctic and Antarctic regions with the dataset used in this study. A total of six features were combined to monitor sea ice, including RESC, RESI, RESD, REWC, REWI and REWD. Although these features have been applied to sense sea ice individually in the previous study, the combination of these six features is firstly adopted to monitor sea ice. Compared to the single observable method, the feature sequences can represent the characteristics of reflecting surface more accurately. Therefore, the ML-based approaches achieve higher accuracies than the single observable thresholding method. It would be worth noting that the input features to ML-based methods are different from the single observable thresholding method. Moreover, the spaceborne GNSS-R dataset used here spans 42 months of the TDS-1 mission, which is larger than those applied in the previous studies. The results from this study are encouraging for the GNSS-R applications of machine learning algorithms. Further research on the effects of oceans winds in the low SIC regions will benefit monitoring sea ice. In addition, the combination of multiple ML-based methods (e.g., DT, RF and SVM) will be explored in our future work. Conceptualization, Y.Z. and T.T. ; methodology, Y.Z. ; software, Y.Z. and T.T. ; validation, Y.Z. and T.T. ; formal analysis, Y.T. ; investigation, Y.Z. ; resources, Y.Z. ; data curation, Y.Z. ; writing--original draft preparation, Y.Z. ; writing--review and editing, Y.Z., K.Y., X.Q., S.L., J.W. and M.S. ; visualization, Y.Z. ; supervision, T.T. and K.Y. ; project administration, T.T. ; funding acquisition, T.T. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Fundamental Research Funds for the Central Universities of China under Grant JZ2020 HGA0087, bythe Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology under Grant DLLJ202001, by the Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University under Grant 19-01-03, by the Natural Science Foundation of Anhui Province, China under Grant 1808085 MD105 and by the National Natural Science Foundation of China under Grant 41871313. The authors would like to thank the TechDemoSat-1 team at Surrey Satellite Technology Ltd. (SSTL) for providing the spaceborne GNSS-R data. Our gratitude also to Ocean and Sea Ice Satellite Application Facility for the sea ice edge product used in comparisons. The sea ice concentration (SIC) data processed by the Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm were obtained from www.meereisportal.de.