## 3 Results
### Sea Ice Monitoring Performance  
The sea ice monitoring models based on DT and RF algorithms are quantitatively assessed using confusion matrices [58] through a comparison with the OSISAF SIE data using the test data. In the field of machine learning and specifically the problem of statistical classification, a confusion matrix [56], also known as an error matrix, is a specific table layout that allows visualization of the performance of a supervised learning algorithm. The confusion matrix is a table with two rows and two columns that reports the number of false positives, false negatives, true positives and true negatives. The error matrices, overall accuracy and kappa coefficient [59] of the agreement are used as indicators to evaluate the performance of the DT and RF models. The performance of the DT and RF models for the Arctic and Antarctic regions are presented in Tables 2 and 3, respectively. The overall accuracy of DT model is 97.51% and 95.46% for the Arctic and Antarctic, respectively, while the RF model produced an overall accuracy of 98.03% and 95.96% for the Arctic and Antarctic, respectively. The producer and user accuracies of sea water are higher than those of sea ice for both models. This may be because the sea ice with a low SIC is more easily misidentified as sea water. When the surface area with both ice and water is driven by wind field, the surface will become rougher, and the sea ice surface is recognized as sea water. Although the DT and RF models obtain similar overall accuracies, the Kappa coefficient of agreement of RF model is slightly higher than that of DT, which indicates that the performance of the RF algorithm is better than that of DT. Although the overall accuracy obtained in this study is slightly lower than that in the previous study, the dataset used here is much larger and the data filter requirement is lower. This indicates the method developed and applied here is of better applicability and generality. When using data only from the initial mission, as we did in our previous study [25], the overall accuracy of this method is 0.22% better than the REWD method we applied there. The previous study [37] indicated that the support vector machine (SVM) outperforms the neural network (NN) and convolutional neural network (CNN) methods for detecting sea ice using spaceborne GNSS-R data. SVMs are capable of operating classification tasks by finding a hyperplane that can best distinguish (with the maximum margin) between different types. NNs are extremely flexible in the types of data they can support. NNs do a decent job at learning the important features from basically any data structure, without having to manually derive features. CNNs are much less flexible models compared to a fully connected network, and are biased toward performing well on image. In order to evaluate the performance of proposed methods, the SVM is adopted for comparison. The sea ice monitoring results obtained by SVM based methods are shown in Table 4. The proposed RF-based sea ice monitoring approach shows better accuracy than the SVM-based method, while the SVM-based sea ice monitoring scheme outperforms the DT-based one. The feature sequences applied in this study are extracted from delay waveforms (NCDW, NIDW and DDW) with different doppler shifts. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Reference classified as** & **Sea ice** & **Sea water** & **Sum** & **User accuracy** \\ \cline{2-5}  & **Sea ice** & 1,270,679 & 31,121 & 1,301,800 & 97.61\% \\ \cline{2-5}  & **Sea water** & 33,945 & 1,402,807 & 1,436,752 & 97.64\% \\ \cline{2-5}  & **Sum** & 1,304,624 & 1,433,928 & 2,738,552 & \\ \cline{2-5}  & **Producer accuracy** & 97.40\% & 97.83\% & & \\ \cline{2-5}  & **Overall accuracy** & & 97.62\% & & \\ \cline{2-5}  & **Kappa coefficient** & & 95.24\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The confusion matrix for the Support Vector Machine (SVM) algorithm using data from Arctic and Antarctic regions.