## 2 Materials and Methods
### Accuracy Evaluation  
To further verify the accuracy of rice extraction on the basis of SAR image, the rice-planting area in the study area was extracted by using object-oriented classification method (segmentation scale 10; the proportion of each band is 1) on the basis of Sentinel-2 optical remote sensing image. The local effect is shown in Figure 20. To verify the reliability of the classification result, 200 validation sample points of four land types were collected from Google Earth, and the classification accuracy of the statistical area of rice in Guangzhou City in 2017 was evaluated by using an error matrix based on TTA Mask. Figure 19: Multi-temporal and coherence coefficient RGB composite map. Figure 20: Local effect map of rice identification result. The classification accuracies of time-series SAR and time-series SAR combined with coherence coefficient are shown in Tables 8 and 9, respectively. The user classification accuracy of Scheme 1 is 78%, whereas that of Scheme 2 is improved by 3%. On the basis of area verification in Table 10, the accuracy of Scheme 2 is improved by 2.05% compared to that of Scheme 1. On the basis of the verification result of the error matrix and statistical area accuracy, the accuracy can be improved significantly after the coherence coefficient is combined with time-series SAR classification. On the basis of the rice area statistical accuracy in Table 10, the accuracy of the rice-planting area extraction of Scheme 2 is significantly higher than that of Scheme 1. Figure 20 shows that the rice-planting area extracted by this scheme basically corresponds to the spatial position extracted by the optical image (Area A in Figure 20). This scheme proves to be practical and has significance for the large-scale monitoring of rice and other food crops. The polarization backscatter coefficient of rice cannot be easily distinguished from vegetation in the SAR image, which leads to error in the extraction of rice and vegetation information. On the basis of InSAR, the coherence coefficient was introduced in this study, which helps to distinguish rice and vegetation information and improve the identification accuracy. Finally, on the basis of the analysis of the overall accuracy and Kappa coefficient of the two schemes, the overall accuracy is improved by 2.74%, and the Kappa coefficient is improved by 0.366 due to the use of coherence coefficient. On the basis of the above analysis, the introduction of coherence coefficient to extract the rice-planting area can significantly improve not only the accuracy of rice identification but also the accuracy of land cover classification. Moreover, it is of great significance for studies on land cover on the basis of SAR images. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Statistical** & **Classification** & **Accuracy\%** \\
**Scheme** & **Area/Hectare** & **Area/Hectare** & **Area/Hectare** \\ \hline Time series & 26191 & 21435 & 81.84 \\ Time series + & 26191 & 21972 & 83.89 \\ coherence coefficient & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Statistical accuracy of rice area. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Artificial Buildings** & **Rice** & **Water Area** & **Vegetation** & **Total** & **User Accuracy (\%, UA)** \\ \hline Artificial Buildings & 170 & 12 & 7 & 11 & 200 & 85.00 \\ Rice & 11 & 156 & 5 & 28 & 200 & 78.00 \\ Water area & 5 & 2 & 188 & 5 & 200 & 94.00 \\ Vegetation & 12 & 21 & 8 & 159 & 200 & 79.50 \\ Total & 198 & 191 & 208 & 203 & 800 \\ Mapping accuracy (\%, PA) & 85.86 & 81.68 & 90.38 & 78.33 & \\ Overall accuracy (OA) = 84.13\% & & Kappa coefficient = 0.7883 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Time-series SAR classification accuracy.