# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 4 Results  
In this section, we report the results of the proposed classifications. First, the 5 folds cross-validation of the RF classifier shows that out of the 319 rice plots only 5 plots were miss-classified as other crops and one non-rice plot was classified as rice. The confusion matrix obtained when performing the 5 folds cross-validation shows an overall accuracy of 99.2%. Then, the performance of the proposed classifications (decision tree and random forest) was assessed by comparing the produced rice map to the national data of the RPG-2017 (Graphical Registered Parcels) provided freely by the French government ([[https://www.data.gouv.fr](https://www.data.gouv.fr)]([https://www.data.gouv.fr](https://www.data.gouv.fr))). The validation is done over all of the classified plots in our study area (9976 plots in total). Each field is checked if it is correctly classified through being compared with the declared rice plots of the RPG data. Table 2 shows the confusion matrix obtained using the simple decision tree, whereas Table 3 presents the confusion matrix produced using the random forest classification. The overall accuracy, Kappa, and F\({}_{1}\) score for both classifiers were calculated. Similar high accuracy is obtained using both proposed methods. The RF classifier produces an overall accuracy of 96.7% whereas the decision tree gives 96.3% overall accuracy. In fact, using the proposed decision tree, 325 rice plots were misclassified as non-rice plots, whereas using the RF classifier 266 rice plots were misclassified as non-rice plot according to the RPG data. Additionally, using the RF classifier 68 non-rice plots were classified as rice, whereas using the decision tree method 47 non-rice plots were classified as rice. Moreover, the metrics' importance was assessed for the random forest classifier. The significant metrics that contributed the most to the classification of the rice plots were analyzed according to the increase in the mean square error (%IncMSE) of the predictions (Figure 8). The results show that the high significant metrics are the variance of the VV/VH and the slope of the linear fitting of the VH, whereas the less significant metrics are the maximum position and standard deviation of the Gaussian fitting of VV/VH. The rice area map of the Camargue region was finally established by applying the generated random forest classifier (Figure 9a). The estimated rice area in 2017 obtained using the proposed classification is 9740 ha. Comparing this area to the rice area from the IGN data of 2011, we observe that the rice cultivation area has significantly decreased in the region from 16,000 ha in 2011 to 9740 ha in 2017 (our estimation). The obtained rice area was then compared to the rice area declared in the RPG data (Figure 9b). Using the proposed classification the rice area was estimated as 9740 ha slightly lower than the declared area (10,015 ha). Thus, the overall accuracy in the rice area estimation reaches 97.3%. According to the proposed classification, the rice area in 2017 occupied approximately 32.5% of the total agricultural area. This percentage of rice crops agrees with the percentage obtained by Moki \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class Value** & **Rice** & **Other Crop** & **Total** & **User Accuracy** \\ \hline
**Rice** & 3120 & 325 & 3445 & 90.5\% \\
**Other Crop** & 47 & 6475 & 6522 & 99.2\% \\
**Total** & 3167 & 6800 & 9967 & \\
**Producer Accuracy** & 98.5\% & 95.2\% & & \\ \hline
**Overall Accuracy** & & & 96.3\% & \\ \hline
**Kappa** & & & 91.5\% & \\ \hline
**F\({}_{1}\) score** & & & 94.3\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix for decisional tree classification. Figure 8: Metrics’ order of importance in the RF classifier.