# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 1 Introduction  
Rice is a staple food resource for more than half of the world population. It plays an important role in the global economy, food security, and water use. In 2017, rice was classified by the FAO (Food and Agriculture Organization of the United Nations) as the second most produced cereal in the world [1]. Moreover, according to the International Food Policy Research Institute, the demand for rice is increasing by 1.8% per year. Thus, obtaining information on the rice's location and distribution is of great importance for food security and water use. Considered as an important reservoir of biodiversity, the Camargue region of South France is the main producer and supplier of rice in France. Rice in Camargue plays an important role in the hydrological balance of the region [2]. In fact, rice crops prevent high levels of salinity in the low lying soils and thus offer a temporary habitat to migratory bird species. Additionally, rice production in the region has a significant impact on the regional economy. Therefore, mapping the spatial extent of the rice is of great importance for the agriculture sector in France. Several studies have reported using remote sensing for land surface monitoring in agricultural areas [3; 4; 5; 6; 7; 8]. This is particularly the case for rice mapping. Among the many optical sensors, MODIS,Landsat, and most recently Sentinel-2 have been used to map irrigated paddy rice [9; 10; 11; 12]. For example, [PERSON] et al. [9] used the time series of MODIS-derived vegetation indices (LSWI: Land Surface Water Index, EVI: Enhanced Vegetation Index, and NDVI: Normalized Differential Vegetation Index) to map paddy rice during the phases of flooding and transplanting. Their study was based on the unique spectral reflectance of the flooded soil-vegetation mix compared to other croplands. Additionally, [PERSON] et al. [13] mapped rice areas in China using MODIS time series products and the support vector machine. The overall accuracy achieved was 0.90 with a kappa coefficient of 0.77. Moreover, [PERSON] et al. [14] developed a phenology-based algorithm to map rice areas over the Poyang Lake Plain, China using the normalized EVI and SAVI (Soil Adjusted Vegetation Index) derived from Landsat-8 images. They achieved an overall accuracy of 96.8% and a correlation coefficient \(R^{2}=0.88\) when comparing the produced map with national statistics. However, the cloud cover limits the use of high spatial resolution optical images for rice mapping, especially in tropical areas where there is cloud cover more than 70% of the time during the rice growing season. On the other hand, several studies investigated the capabilities of SAR (Synthetic Aperture Radar) data for rice mapping and monitoring. These studies assessed the potential of SAR data in the L-band [15], C-band [16; 17], and X-band [18] for rice monitoring. In many studies, rice fields have been mapped based on the low radar backscattering signal of rice plots in the beginning of the season when the fields are flooded [19; 20; 21]. Other studies used the temporal behavior of the radar backscattering signal in several polarizations to map paddy rice [22; 23; 24]. [PERSON] et al. [24] used random forest classification to map rice areas using a time series of Sentinel-1 SAR images in Hanoi, Vietnam. In their study, the highest overall accuracy (\(93.5\%\pm 1.33\)) was achieved using the ratio of the VV and VH polarizations. Moreover, [PERSON] et al. [23] used the ratio of the HH and VV polarizations derived from the Cosmo Sky-Med SAR data to map rice areas in the Mekong Delta, Vietnam. Recently, [PERSON] et al. [25] applied a deep recurrent neural network (DNN) on a Sentinel-1 SAR time series to classify the agricultural land cover in Camargue, France. They reached an overall accuracy of 89.6% for agricultural classification. The recently launched Sentinel-1 SAR satellite offers a powerful tool for agricultural area classification and monitoring under various weather conditions.