# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 4 Sea Ice Extent Mapping and Validation
### SVM Model Experiments  
In order to identify the best scheme for combining HSCAT, CSCAT, and ASCAT data, we performed seven SVM model experiments (as shown in Table 3). The experiments used the same SVM classifier method, but with different input data, and the experiments were divided into three categories for the sake of comparison. To evaluate the performance of the experiments, 100,000 samples were randomly picked as the test dataset for each experiment.  
Category 1 compares the results from HSCAT, ASCAT, and joint usage of HSCAT and ASCAT. For HSCAT data, four parameters (the same as the well-known QuikSCAT RL-N algorithm) are used. For ASCAT data, three parameters are used. In addition, to make the results more comparable, we only work on the pixels for which both HSCAT and ASCAT data are available.  
In category 2, the results from CSCAT, ASCAT, and joint usage of CSCAT and ASCAT are compared. Both CSCAT and ASCAT are able to provide incidence-angle-dependent backscatter measurements, thus making available the parameters of \(K_{VV}\) and \(BR\).  
Category 3 assembles all of the parameters from the HSCAT, CSCAT, and ASCAT measurements. In categories 1 and 2, the same pixels of daily sea ice maps were used for different experiments. Thus, we are solving the same problem (sea water and ice discrimination) using the same SVM method, but with different model inputs.  
Figure 3: The flowchart of sea ice monitoring based on SVM classifier with multisource scatterometer measurement.  
Figure 4a,b show the overall accuracy (OA) of the SVM model derived from the test set in the Arctic for Antarctic, respectively. It can be observed that the OA of the SVM model with only ASCAT as inputs (i.e., experiments II and V) is clearly lower than that of the other models. The OA of the models with data from a single Ku-band (experiments I and IV) scatterometer as inputs is higher than the models with data from a single C-band scatterometer as inputs. The model with only CSCAT data as inputs (experiment IV) performs slightly better than the model with only HSCAT data as inputs (experiment I). For model experiments with joint usage of scatterometer data (experiments III, VI, and VII), the OAs of models with both Ku-band and C-band scatterometer data are higher than those of models using Ku-band or C-band scatterometer data alone. It is noted that, with sufficient training samples, the SVM model with all available scatterometer data as inputs show the best performance among all seven model experiments.  
In summary, the SVM model (i.e., experiment VII) in which all available characteristic parameters from HSCAT, CSCAT, and ASCAT measurements were used as inputs demonstrated the best performance and will be used hereafter for sea water and ice discrimination.  
Figure 4: _Cont._  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Category** & **ID** & **Parameters from Scatterometer Data** \\ \hline \multirow{4}{*}{1} & I & HSCAT: \(PR\), \(\sigma^{0}_{HH}\), \(\Delta\sigma^{0}_{HH}\), \(\Delta\sigma^{0}_{VV}\) \\ \cline{2-3}  & II & ASCAT: \(\sigma^{0}_{VV}\), \(\Delta\sigma^{0}_{VV}\)\(K_{VV}\) \\ \cline{2-3}  & III & HSCAT + ASCAT \\ \hline \multirow{4}{*}{2} & IV & CSCAT: \(PR\), \(\sigma^{0}_{HH}\), \(\Delta\sigma^{0}_{HH}\), \(\Delta\sigma^{0}_{VV}\), \(K_{VV}\) \\ \cline{2-3}  & V & ASCAT: \(\sigma^{0}_{VV}\), \(\Delta\sigma^{0}_{VV}\), \(K_{VV}\) \\ \cline{2-3}  & VI & CSCAT + ASCAT + \(BR\) \\ \hline
3 & VII & HSCAT + ASCAT + CSCAT + \(BR\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Categories of SVM model experiments.