# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 4 Results and Discussion
### Winter Wheat Mapping  
#### 4.2.1 SAR Image Classification and Accuracy Assessment  
The classification accuracy of winter wheat using RF and SVM classifiers are shown in Table 4. Compared to the RF using different combinations of SAR data, the results of the SVM were generally slightly higher in terms of overall accuracy and kappa coefficient; moreover, RF cost more time than SVM in all combinations (see Figure 5). However, RF obtained better winter wheat classification results than SVM in terms of the F1 measure. As shown in Table 4, all F1 measures for winter wheat using the single polarization were lower than 75% and the performance of VV polarization was better than that of VH polarization, which indicates that two single polarizations could not meet the winter wheat classification accuracy requirements. The winter wheat classification accuracy using the SAR data combination of VV + VH was much better than that of VH and VV, with an overall accuracy of 91.45% and a kappa coefficient of 0.8729 (F1 measure = 85.72), which was mainly due to winter wheat VV polarization and VH polarization having different scattering mechanisms, and the combined polarization (VV and VH) rather than VV or VH provided richer wheat radar wave scattering information, effectively improving the winter wheat classification accuracy. In order to fully explore the Sentinel-1A data to improve the classification accuracy, texture and coherence information were extracted and added to the classification. As shown in Table 4, the classification accuracy using backscatter intensity data was higher than that of the texture and the coherence images. Although the overall accuracy and kappa coefficient of the texture images were higher than that of the coherence images, the F1 measure of the winter wheat was lower than the coherence images. This suggests that backscatter intensity was more important for classification than coherence and texture information. When the coherence images were added to the backscatter intensity images, the overall accuracy increased from 91.45% to 94.94% for RF (F1 measure = 92.35%) and from 89.82% to 96.19% for SVM (F1 measure = 93.10%), respectively. This result was consistent with earlier studies using the combination of backscatter intensity and coherence images [21; 22]. Furthermore, when the texture images were added to the backscatter intensity images, the classification accuracy also increased. This indicates that coherence and texture information are useful parameters for the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**F1 Measure (\%)**} & \multicolumn{3}{c}{**Overall Accuracy (\%)**} & **Kappa** \\ \hline
**Classifier** & **ID** & **Wheat** & **Rapeseed** & **Forest** & **Urban** & **Water** & & \\ \hline RF & a & 50.00 & 61.80 & 69.80 & 80.42 & 95.58 & 82.26 & 0.7351 \\  & b & 74.48 & 64.47 & 87.25 & 88.70 & 96.99 & 90.10 & 0.8525 \\  & c & 85.72 & 78.02 & 87.0  
classification of SAR data. [PERSON] et al. (2018) also showed that the use of texture images can improve the classification accuracy of SAR data. However, the classification accuracy using the combination of coherence and texture images was lower, with an overall accuracy of 73.82% for RF, and 85.19% for SVM, respectively. The best results of winter wheat were obtained using an SAR data combination of VV + VH + C + T, with the overall accuracy and kappa coefficient up to 95.95% and 0.9399 for SVM (F1 measure = 93.61), respectively, which was lower than the F1 measure for RF. In general, when the target accuracy is greater than 85%, the classification of this crop is reliable (26; 63; 64). Therefore, only the use of SAR data can meet the winter wheat classification accuracy requirements (with F1 measure than 85%). That is to say, the optical image can be replaced by SAR data in order to distinguish winter wheat with complex planting structures.