# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 5 Discussion  
In this study we mapped rice areas in the Camargue region using Sentinel-1 SAR backscattering time series and two classification approaches: random forest and decision tree. We first analyzed the SAR backscattering coefficient over the entire agricultural crop existing in the area and showed that the rice plots have a specific temporal profile in VH polarization and in the VV/VH ratio. Then, we mapped then rice areas based on metrics derived from the VH and VV/VH time series. The obtained results were finally compared to existing national data. This validation indicated that the proposed approach has good performance where the overall accuracy was 96.6% and Kappa coefficient was better than 92.5% (using RF classifier). To properly assess the quality of the obtained results, we compared the classification results with results obtained by other classification methods using the same dataset. Through literature,  
Figure 9: (**a**) Rice areas in Camargue in 2017 using our proposed RF classifier, (**b**) Rice areas in Camargue in 2017 declared by the RPG-2017. The background in both maps is a SAR image in VV polarization. [PERSON] et al. [25] classified the agricultural areas in Camargue, France by applying the deep recurrent neural network (DNN) on a Sentinel-1 SAR time series. Rice crop was one of the classified agricultural crops. They showed that the overall accuracy of the agricultural classification using the DNN is 89.6%. In particular, the confusion matrix obtained using the 5 folds cross-validation of the DNN showed that the producer accuracy of the rice class reaches 95.7% and the user accuracy reaches 97.5%. In their study, they also tested the RF classifier using the S1 time series. The confusion matrix obtained using the RF classifier shows that the producer accuracy for rice class reached 92.7% and the user accuracy reached 97.6% when performing the 5 folds cross-validation. On the other hand, the 5 folds cross-validation of the RF classifier in our approach shows that the producer accuracy reaches 99.0% while the user accuracy reaches 98.4%. Figure 10 presents the classified rice areas produced using their proposed approach (using the DNN). Since their approach uses the same dataset as our study (Sentinel-1 time series and ground data) and was accomplished for the same year (2017), a comparison was also performed between the obtained rice class of [PERSON] et al. [25] and the RPG-2017. The 9976 plots classified in our proposed approach were checked for being rice or other crop in the agricultural land cover map proposed by [PERSON] et al. [25] and then compared to the previous results obtained for RPG data. Table 4 presents the confusion matrix produced by comparing [PERSON]'s rice areas and RPG rice areas based on the classified plots in our approach. The overall accuracy of the Ndikumana rice classification when compared to RPG is 94.4% slightly lower than the accuracy obtained using our approach (96.6%). Both the user and producer accuracies are higher for our approach (92.3% and 97.9% respectively) compared to [PERSON]'s approach (88.3% and 95.2% respectively) when comparing to the RPG data. Based on the comparison of both rice areas to the RPG data, we can conclude that our proposed method remains a powerful tool to map paddy rice areas. The complexity of the method used by [PERSON] et al. [25] to produce the land cover map and the simplicity of our method with better accuracy makes our proposed approach powerful for rice area mapping. However, it is good to mention that [PERSON] et al. [25] classified the 11 agricultural classes, rather than only the paddy rice. Figure 10: Rice areas in Camargue in 2017 extracted from [PERSON] et al. [25] land cover map. The background is a SAR image in VV polarization. Several studies have already used traditional machine learning algorithms for paddy rice mapping such as RF, Support Vector Machine (SVM), and decision tree [24; 30; 31; 32; 33; 34]. Recently, [PERSON] et al.