## 4 Experimental Result and Discussion
### Rice Mapping  
Ten SIA images with RoN of 55 acquired from 2 February 2018, to 21 May 2018 were used for rice mapping in Quang Ninh while 10 SIA images from 4 February 2018, to 23 May 2018, with RoN of 91, were used for the remaining 10 provinces. Four datasets of features VV, VH, \(\frac{VV-VH}{VH+VH}\) and VVVH were investigated to develop the best classification model for rice mapping. We found that the VVVH temporal backscatter feature outperformed the rest of feature datasets with 5.43% higher OA, 5.25% higher F1 score and 11.25% higher Kappa coefficient on average (Table 4). Rice sensitivity is quite similar to VV and VH individually since the recalls of VV, VH and VVVH are similar for rice (0.84, 0.85 and 0.84, respectively in Table 5). However, the usage of both VV and VH temporal data is better than any individual one in distinguishing rice from the other classes (i.e., the highest precision). The combination of two channels following equation \(\frac{VV-VH}{VV+VH}\) resulted in the most ineffective with 6.67% lower OA, 6.75% lower F1 Score and 13.75% lower Kappa coefficient. Thereby, time-series of VV and VH individually had good features that were even better when combined to classify rice. Figure 5: The average backscatter of 5 land-cover classes in the RRD. The VH-, and VV-average backscatter coefficient are respectively shown in (**a**,**b**). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**STD**} & \multicolumn{2}{c}{**Max**} & \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Diff**} \\ \cline{2-11}  & **VV** & **VH** & **VV** & **VH** & **VV** & **VH** & **VV** & **VH** & **VV** & **VH** \\ \hline Forest & \(-7.89\) & \(-13.97\) & \(0.33\) & \(0.41\) & \(-7.24\) & \(-13.29\) & \(-8.67\) & \(-14.77\) & \(-1.43\) & \(-1.47\) \\ Rice & \(-10.73\) & \(-18.21\) & \(2.60\) & \(3.67\) & \(-7.11\) & \(-13.89\) & \(-17.16\) & \(-26.46\) & \(-10.06\) & \(-12.57\) \\ Urban & \(-4.20\) & \(-12.02\) & \(0.40\) & \(0.25\) & \(-3.32\) & \(-11.45\) & \(-4.92\) & \(-12.47\) & \(-1.60\) & \(-1.02\) \\ Bare land & \(-10.09\) & \(-16.34\) & \(0.78\) & \(0.82\) & \(-9.00\) & \(-15.02\) & \(-12.65\) & \(-19.28\) & \(-3.65\) & \(-4.27\) \\ Water & \(-18.95\) & \(-27.43\) & \(0.96\) & \(1.01\) & \(-16.97\) & \(-24.76\) & \(-21.37\) & \(-28.56\) & \(-4.40\) & \(-3.80\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical measures of VV and VH channels for different land surfaces. The quality of the best rice map, produced by the VVVH dataset, was evaluated even further by comparison with statistics from the GSO at the provincial level (Table 6). The error of the whole region is approximately 2.37%, (12,544 hectares). The estimated rice area is quite similar for most of the provinces in the RRD. However, rice maps in Quang Ninh cause the most differences (10,665 hectares). By visual inspection, we realized that in some high mountain areas like Quang Ninh, terrain shadow might be misclassified as rice. Figure 6 shows the strong agreement of estimated rice with GSO statistics in 11 provinces in the RRD with a high correlation coefficient at 0.97. The entire rice map for the winter-spring crop in 2018 presents the popularity of rice in the RRD. However, Quang Ninh is the largest area (610,200 ha) in the region but its rice area is the lowest one (see Table 6 and Figure 7). Rice is sparsely grown in hilly areas and islands compared to the plain.