# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## 3 Results
### Optimal Polarization Images
We have therefore, supposed the VH polarization to provide a better discrimination of Rice from other land-cover categories whose temporal backscatter profiles are generally stable during the paddy rice growing season in the area under investigation. Results on the determination of the optimal polarization channel based on two-class signature separability measures are given in Table 4. Results are computed using Equation (2) in Section 2.6. Figure 4: Temporal backscatter profiles of Rice subclasses at both polarizations: (**a**) Rice A; (**b**) Rice B. Data points correspond to Sentinel-1A images of Shanghai acquired on 9 June, 8 July, 1 August, 25 August and 18 September in 2015, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Rice A**} & \multicolumn{3}{c}{**Rice B**} & \multicolumn{3}{c}{**Water**} & \multicolumn{3}{c}{**Built**} & \multicolumn{3}{c}{**Trees**} & \multicolumn{3}{c}{**Others**} \\ \cline{2-13} \multicolumn{1}{c}{_Image_} & \(\mu\) & \(s\) & \(\mu\) & \(s\) & \(\mu\) & \(s\) & \(\mu\) & \(s\) & \(\mu\) & \(s\) & \(\mu\) & \(s\) \\ \hline
9 June & \(-12.6\) & \(2.0\) & \(-9.8\) & \(2.4\) & \(-15.4\) & \(2.9\) & \(-0.9\) & \(3.6\) & \(-7.5\) & \(0.6\) & \(-10.7\) & \(3.7\) \\
8 July & \(-8.3\) & \(1.5\) & \(-11.0\) & \(2.0\) & \(-14.4\) & \(2.6\) & \(-2.0\) & \(3.2\) & \(-7.0\) & \(1.0\) & \(-9.9\) & \(4.1\) \\
1 August & \(-9.7\) & \(1.7\) & \(-9.3\) & \(1.7\) & \(-13.7\) & \(1.9\) & \(-2.5\) & \(3.2\) & \(-7.3\) & \(1.1\) & \(-10.4\) & \(3.5\) \\
25 August & \(-10.9\) & \(1.5\) & \(-10.2\) & \(2.2\) & \(-15.3\) & \(2.0\) & \(-2.2\) & \(3.2\) & \(-7.0\) & \(0.8\) & \(-10.2\) & \(4.0\) \\
18 September & \(-12.5\) & \(2.0\) & \(-13.4\) & \(1.8\) & \(-14.8\) & \(2.6\) & \(-2.3\) & \(3.2\) & \(-7.6\) & \(0.9\) & \(-10.4\) & \(3.9\) \\ \hline \hline \end{tabular} \(\mu\) and \(s\) are the mean and standard deviation, respectively, of backscatter (dB) at corresponding class and date.  
\end{table} 
Table 3: Temporal backscatter coefficients for all land-cover classes at VV polarization. From Table 4, the class pair \"Rice and Others\" is not linearly separable as the latter is a combination of surface features (bare soils, asphalt pavements and grassland) which generally exhibit similar (low) backscatter values especially at the start of the rice growing season. However, as our mapping strategy is based on multi-temporal data, these classes can be separated based on the increase in temporal backscatter of rice at the start of the growing season, as opposed to the relatively linear backscatter profile of \"Others\" at periods corresponding to the rice growing season. In general, a separability value below 0.8 is regarded not large enough to separate two classes. Between 0.8 and 1.5, the quality of the separation between the two classes is regarded as average. Values above 2.0 indicate almost complete separability between classes [53; 54]. From Table 4, it can be observed that with exception of the class pairs \"Water and Built\" and \"Built and Trees\", the VH polarization produces better inter-class signature separability. Based on the most sustained increase in paddy rice backscatter at the VH polarization, and the superiority of the VH over the VV channel in the two-class separability measure, we have, therefore, proposed the VH polarization as the optimal polarization and with which the optical indices are combined for the mapping of rice fields and the other land-cover categories in this study. It has also been observed in [52] that at C-band, cross (VH) polarization produces better crop classification results than the like (VV) polarization, and that the use of dual polarization does not further improve the classification results. Figure 5 shows the temporal backscatter profiles of the two Rice subclasses (Rice A and Rice A) and the other broad land-cover categories at the VH polarization.