# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 6 Conclusions  
This study investigates the RF and SVM-based classifiers for Arctic Sea ice classification using the one-against-all binarization, which converts a multi-classification into several binary classification problems. Thus, the classification is implemented in a two-step way. The first step aims to discriminate OW from sea ice (FYI and MYI), which is further classified in the second step. The selected data periods include February to April and November to December in 2018, during which the information of different surface types (OW, FYI, and MYI) is available and can be used for comparison. Through validating  
Figure 16: (**a**) Arctic daily sea ice extent trend in 2018. The average sea ice extent per month is depicted by the blue circle. The maximum and minimum sea ice extent are marked by a red cross-section and green circle. The data used in this study are collected during two periods which are marked by magenta and blue rectangles, respectively. The data availability during the first period (February to May) is marked by red dots in the magenta rectangle. The data availability during the second period (October to December) is marked by blue dots in the blue rectangle. (**b**) Arctic monthly sea ice extent trend in 2018. The upper and lower value of the error bar represents the maximum and minimum sea ice extent per month. Sea ice extent data are obtained from the National Snow and Ice Data Center on 15 August 2021 (NSIDC, [[https://nsidc.org/data/seaice_index/archives](https://nsidc.org/data/seaice_index/archives)]([https://nsidc.org/data/seaice_index/archives](https://nsidc.org/data/seaice_index/archives)), accessed on 11 September 2021). Figure 15: The (**a**) accuracy and (**b**) _kappa_ coefficient of FYI-MYI classification using data of each month as the training set in turn. The top and middle plots in (**a**) represent the accuracy of RF and SVM respectively, while the lowest plot is the accuracy of RF minus that of SVM. The top and middle plots in (**b**) represent the kappa coefficient of RF and SVM respectively, while the lowest plot is the kappa coefficient of RF minus that of SVM. against OSISAF SIT maps, the overall accuracy of the RF and SVM for OW-sea ice classification is up to 98.83% and 98.60%, which is comparable to results from some previous studies using TDS-1 data [15; 30; 38; 39; 40]. Hereafter, the FYI-MYI classifier is modeled in the second using the samples of sea ice that include FYI and MYI. The overall accuracy of RF and SVM for classifying FYI and MYI is 84.82% and 71.71%, respectively, which are lower than those of the OW-sea ice classifier. Moreover, the influence of ice growth and melt for sea ice classification is evaluated through a cross-validating strategy, which applies each month of data as the training set and the remaining four months of data as the test set. To the best of the author's knowledge, the RF and SVM are firstly used for classifying FYI and MYI using the spaceborne GNSS-R data. The results indicate that RF and SVM-based GNSS-R have great potential in sea ice classification. This study demonstrates that the great potential of GNSS-R for classifying sea ice types, which can be an effective and complementary approach for remotely sensing sea ice. In future studies, more GNSS-R features, ML algorithms, and environmental effects (such as ocean wind) should be investigated to improve the accuracy of classifying FYI and MYI. Conceptualization, Y.Z. and T.T. ; methodology, Y.Z. ; software, Y.Z., J.L. and T.T. ; validation, Y.Z. and T.T. ; formal analysis, Y.Z. ; investigation, Y.Z. ; resources, Y.Z. ; data curation, Y.Z. ; writing--original draft preparation, Y.Z. ; writing--review and editing, Y.Z., K.Y., L.W., X.Q., S.L., J.W. and M.S. ; visualization, Y.Z. ; supervision, T.T. and K.Y. ; project administration, Y.Z. ; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.