# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 4 Sea Ice Extent Mapping and Validation
### Evaluation of Sea Ice Distribution Model Precision  
A more detailed evaluation of the selected SVM model (i.e., experiment VII), which inputs all available characteristic parameters from HSCAT, CSCAT, and ASCAT measurements, is performed. Again, we use NSIDC SIC data as reference data. The evaluation indicators include accuracy, precision, recall, and F1 score. The accuracy gives the overall prediction accuracy of the model. The precision is defined as the proportion of correctly predicted samples to total samples. The recall represents the probability of the samples being correctly predicted. The F1 score is an index used to measure the accuracy of a classification model that takes into account both the precision and recall of the classification model.  
It should be noted that, in the confusion matrix, the true positive (TP), true negative (TN), false positive (FP) and false negative (FN) is defined differently for each type. Therefore, the values of any precision, recall and F1 scores can be defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
\[F1\;score=\frac{2*Precision*Recall}{Precision+Recall} \tag{8}\]  
Figures 5 and 6 show the time series of sea ice monitoring model evaluation indicators for the Arctic and Antarctic for the years 2019 to 2021, respectively. Table 4 presents the average classification accuracy, where an overall accuracy of 98.41% was obtained for the Antarctic and 97.15% for the Arctic; the accuracy of the model in the Antarctic region is higher than in the Arctic. In the Arctic, the precision for sea ice was slightly lower in summer and early autumn than in the other seasons. Similarly, the precision for open water in summer was significantly lower than that in other seasons in the Antarctic. This may be due to the fact that melting sea ice in summer increases the content of seawater in sea ice and increases its dielectric constant [6; 13], causing the backscattering coefficient of sea ice to be similar to that of seawater, meaning that the model has a lower sea ice identification rate in summer.  
Figure 4: The overall accuracy of different SVM model experiments in (**a**) Arctic and (**b**) Antarctic regions.  
Figure 5: Time series of model performance in Arctic for the period from 2019 to 2021: (**a**) overall accuracy; and precision, recall and F1 scores for (**b**) open water and (**c**) sea ice.  
Figure 6: Time series of model performance in the Antarctic for the period from 2019 to 2021: (**a**) overall accuracy; and precision, recall and F1 scores for (**b**) open water and (**c**) sea ice.  
Figure 7a shows an example of the sea water and ice discrimination results (26 September 2020) using the trained SVM model with multisource scatterometer data as inputs. The NSIDC SIC data on the same date are shown in Figure 7c. Overall, the sea water and ice are well distinguished. However, some pixels in the open sea, closed ice, or near the ice edge are misclassified. Therefore, a residual classification error correction processing is needed to improve the outputs of the SVM model.