# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 5 Discussion
[31] used the Land Surface Temperature and phenological parameters derived from the NDVI time series of Landsat 8 images (fused with MODIS NDVI to overcome the cloud limitation) with a convolutional neural network approach to map rice areas in the Dongting Lake Area of China. The overall accuracy obtained was 97.1%. On the other hand, when using the classical machine learning methods (SVM and RF) their accuracy decreased to 90.6% and 89.4%, respectively. They report in their study that rice and other crops are often misclassified when using only the spectral information of optical images. When the spectral features were only considered, rice plots were misclassified as grassland and other vegetation type. This limitation is not present in our study as we were able to accurately classify rice areas in the presence of other crops especially those grown in summer (sunflower, grassland... ). In addition, [PERSON] et al. [30] combined the VH polarization of Sentinel-1A images with NDVI and Modified NDWI derived from Landsat-8 images to map rice areas in southeast china. Using the decision tree approach, they reached an overall accuracy of 85% when using both SAR and Optical data. Using only S1A images the overall accuracy obtained in their study was 79%. The study site examined was mainly composed of urban, water, and tree areas. Moreover, the data fusion of optical and radar time series has been also performed in the study of [PERSON] et al. [32] where they mapped rice areas by combining Landsat-8 images with SAR data of RADARSAT and ALOS/PALSAR on two sites in South Korea. The highest accuracy (98.7%) was obtained when using a fusion of SAR and optical data with a digital elevation model and applying the SVM classification. The land cover in the studied zone was mainly composed of rice, grains, pasture, bare soil and vineyards. A common limitation among these discussed studies is the cloud cover that limits the availability of optical images during the rice growing cycle. Using only SAR data, [PERSON] et al. [33] mapped paddy rice using multi-temporal ALOS/PALSAR imagery in southeast China. The studied area was mainly composed of rice, dryland crops (e.g., rapeseed, vegetables) and orchards. They obtained 90% user accuracy and 76% producer accuracy when applying the SVM classifier. [PERSON] et al. [24] have mapped rice areas using Sentinel-1A time series in Hanoi, Vietnam by applying the random forest classifier. The highest overall accuracy (93.3%) was obtained when using the VV/VH ratio. [PERSON] et al. [34] also mapped rice areas over six different study sites using S1 temporal series by applying the decision tree. They chose the VH polarization due to the high dynamic range of backscatter over rice areas. They achieved an overall accuracy of 83%. In their study they recommend that increasing the temporal density of the used Sentinel-1 images would help better increase the accuracy of the classification. Most studies tend to use classical machine learning methods applied on optical or/and SAR data in order to map rice areas. Among the several discussed studies, our proposed method remains powerful since it combines the high accuracy, the simplicity of the applied method and the availability of the used dataset. The obtained accuracy is equivalent and even better than accuracies obtained by complex methods that may require large input datasets. Although the supervised classification highly depends on the study site (mainly on existing crop types) and ground data, the proposed approach could be \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class Value** & **Rice** & **Other Crop** & **Total** & **User Accuracy** \\ \hline
**Rice** & 3042 & 403 & 3445 & 88.3\% \\
**Other Crop** & 153 & 6369 & 6522 & 97.6\% \\
**Total** & 3159 & 6772 & 9967 & \\
**Producer Accuracy** & 95.2\% & 94.0\% & & \\ \hline
**Overall Accuracy** & & 94.4\% & & \\ \hline
**Kappa** & & 87.5\% & & \\ \hline
**F\({}_{1}\) score** & & 91.6\% & & \\ \hline \hline \end{tabular}
\end{table}