# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 5 Conclusions  
In this study, we performed sea ice and water discrimination based on multisource scatterometer measurements using an SVM model. In contrast to previous studies, multisource scatterometer measurements from the C-band MetOp/ASCAT, Ku-band HY-2B/SCAT, and CFOSAT/CSCAT were used in combination. The scatterometer measurements were used to determine characteristic parameters, including polarization ratio (\(\sigma^{0}_{VV}/\sigma^{0}_{HH}\)), horizontal and vertical polarization backscatter measurements (\(\sigma^{0}_{HH}\) and \(\sigma^{0}_{VV}\)), the standard deviations of horizontal and vertical polarization measurements (\(\Delta\sigma^{0}_{HH}\) and \(\Delta\sigma^{0}_{VV}\)), the dependence of \(\sigma^{0}_{VV}\) on incidence angle (\(K_{VV}\)), and the newly proposed band ratio (\(\sigma^{0}_{VV-Ku}/\sigma^{0}_{VV-C}\)). SVM is a supervised classification method that can handle linear, nonlinear, and high-dimensional samples and produce good generalizability. Therefore, the SVM classifier was selected for sea ice monitoring in this study. In order to determine the best scheme for the use of these multisource scatterometer data, seven SVM model experiments (see Table 3)  
Figure 12: Comparison of acquisition of data at daily and half-day time intervals in the Artic for October 2020: (**a**) data coverage; (**b**) sea ice area. Figure 13: Comparison of acquisition of data at daily and half-day time intervals in the Antarctic for October 2020: (**a**) data coverage; (**b**) sea ice area. were performed. We found that the SVM model using all available scatterometer data as inputs performed best among the seven model experiments. In addition to the SVM outputs, we employed image erosion/dilation techniques and the area growth method to reduce the number of misclassifications of sea water and ice. Using our sea water and ice discrimination algorithm, we produced sea ice products for the years from 2019 to 2021; and the results obtained for sea ice extent and edges were verified against NSIIDC SIC and Sentinel-1 SAR images. The verification results show that the multisource scatterometer sea ice extent lay between the NSIIDC SIC 15% and 30% sea ice extent, and there was good agreement with the NSIIDC SIC 15% sea ice extent while showing similar seasonal trends to the sea ice area of the NSIIDC SIC data. The multisource scatterometer sea ice edge showed a good correlation with the sea ice edge determined on the basis of the SAR images. We also tested the possibility of providing shorter-time-interval (i.e., half-day) sea ice products on the basis of multi-source scatterometer data, and we found that the half-day coverage was above 97%, while half-day sea ice area was in good agreement with the daily sea ice area. In conclusion, this study verified the capability of multisource scatterometer measurements for the monitoring of polar sea ice. In the near future, the ability of scatterometer measurements for the monitoring of polar sea ice will be enhanced, as a result of the enhanced configurations of recently launched (FY-3E/WindRAD) and planned scatterometers in terms of radar frequency, polarization, incidence angle, and spatial resolution. Studies on sea ice type classification in polar areas using multisource scatterometers will be introduced in our future work. Conceptualization, C.X. and Z.W. ; methodology, C.X. and Z.W. ; software, C.X. ; validation, Z.W. and C.X. ; formal analysis, C.X. ; investigation, C.X. and Z.W. ; resources, Z.W. ; data curation, C.X. and Z.W. ; writing--original draft preparation, C.X. and Z.W. ; writing--review and editing, Z.W., X.Z., W.L., Y.H. and C.X. ; visualization, C.X. ; supervision, Z.W., W.L., X.Z. and Y.H. ; project administration, Z.W. and Y.H. ; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Key Research and Development Program of China (2021 YFC2803301). Not applicable.