# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 5 Discussion  
In order to analyze the influence of sea ice growth and melt on sea ice classification and the robustness of the sea ice classification method, another experiment is implemented. Each month of data is used as a training set individually, and the remaining four months of data are used as a test set. The strategy is similar to k-fold cross-validation [62] usually applied in ML. However, the k-fold cross-validation is to split the dataset into k parts randomly, which would eliminate the seasonal effects. Firstly, the data in February are used as the training set, and the data in March, April, November, and December are used  
Figure 13: Parts of classification results of random forest (RF) and support vector machine (SVM) classifiers. Zoomed (**a**) RF and (**b**) SVM results of 10,000 samples for OW-sea ice classification. Zoomed (**c**) RF and (**c**) SVM results of 1000 samples for FYI-MYI classification. In each figure of (**a**) to (**d**), the top plot represents the predicted types of the classifier, the middle one is the reference types from OSISAF SIT and the lowest one is the comparison results between predicted and reference types. OW-ice means the OW is misidentified as sea ice, and ice-OW means the sea ice is misclassified as OW. FYI-MYI means the FYI is misidentified as MYI, and MYI-FYT means the MYI is misclassified as FYI. as the testing set in turn. Hereafter, the data in Mar, April, November, and December are used as training set successively. The space distribution of classification results is presented in Appendix A (Figures A1-A10) and Appendix B (Figures A11-A20). The accuracy and _kappa_ coefficients of OW-sea ice and FYI-MYI classification are shown in Figures 14 and 15, which indicate that the overall accuracy of OW-sea ice changes less than that of FYI-MYI classification. This may be caused by the change of samples distribution as the proportion of OW and sea ice is relatively stable, while that of FYI and MYI changes more easily with ice growth and melt. As shown in Figure 14, the accuracy of OW-sea ice classification changes slightly from February to April and then drops from April to November, followed by a larger increase from November to December. The accuracy in November reaches the lowest point among all tested months. Figure 15 demonstrates that the general changing trend of accuracy for FYI-MYI classification is similar to that for OW-sea ice classification. This can be explained in combination with the sea ice extent trend in 2018, shown in Figure 16. As shown in Figure 16, the sea ice extent reached the maximum and minimum on 14 March 2018 and 21 September 2018, respectively. The sea ice extent increases firstly to the peak sea ice extent and then decreases slightly from March to April, while it increases continuously from November to December. Moreover, among five months of use, the ice extent in November is the smallest (Figure 16b). The increase and decrease of sea ice extent can be regarded as ice growth and melt process, respectively. The ice extent during February to April changes less than that during November to December since November is a period of the early stage of winter. The sea ice extent during February to April is relatively stable. From February to March, the FYI grows and the presence of ocean water in FYI becomes less, which results in less misclassification for FYI. During the melting process from March to April, the surface of FYI can be more easily affected by ocean winds due to the decrease of sea ice concentration of FYI. This may lead to more misclassification of FYI to MYI. The overall accuracy for OW-sea ice and FYI-MYI classification in November is the smallest. The explanation is that the sea ice extent is relatively low in November when newly added sea ice is mostly surrounded by ocean water, which leads to more misclassifications. With  
Figure 14: The (**a**) accuracy and (**b**) _kappa_ coefficient of OW-sea ice classification using data of each month as the training set in turn.