## 1 Introduction  
Agriculture is the main consumer of water with about 70-75% of the world's global freshwater [1, 2, 3] dedicated to irrigation. Irrigated lands almost doubled over the last five decades [1, 4] to cover about 20% of the usable agricultural area increasing the water resources depletion especially in areas already facing water scarcity [5; 6]. The latest is unfortunately expected to worsen in the future due to the combined effect of climate change, population growth and agricultural intensification [7]. In the semi-arid Mediterranean region, water consumption through irrigation rises up to 80-90% with an efficiency lower than 50% in several countries. An action on agricultural water demand thus represents the main lever for saving water in the region [8]. Within this context, an accurate knowledge of irrigation water amounts and timing is a prerequisite for improving the efficiency of water use by agriculture at the scales of the decision making: (1) the plot for the farmer who plans its irrigations on a day-to-day basis, (2) the watershed for the basin authorities who distribute water to the different sectors at the seasonal scale and plan the development of the basin and (3) the national territory for the state which defines public policies. The water amounts attributed to the agricultural sector are quite well known at the seasonal scale for the whole watershed by the basin authorities and on a monthly basis for each irrigation districts by the agricultural offices. Yet, irrigation amount and timing are poorly known at the field scale because it is usually not metered neither by the farmers nor by the agricultural office that at best, can provide these information at the farm scale on a monthly basis [8; 9]. Nevertheless, some in situ observation exists. Some relies on the relationship between power demand and pumping rate of pumping stations [10] while others rely on statistical surveys but these latter data are associated to several error sources mainly a self-reporting bias and a low spatial accuracy and temporal resolution [11; 12]. By contrast, the increasing availability of remote sensing observations with high spatial and temporal resolution, specifically since the launch of the Copernicus constellation, offers a unique opportunity to advance in irrigation retrieval from space at the field scale. Mapping irrigated fields has been the first attempt to retrieve irrigation information from space. Most of the approaches relied mainly on optical indices such as NDVI [12; 13; 14], the normalized difference water index NDWI [12], the Greenness index [15] and the Land Surface Water Index [16]. The potential of microwaves in irrigation mapping is actually related to their sensitivity to SSM through the dielectric properties of the soil [17; 18]. Several studies have also demonstrated the potential of microwave observations including SSM products for irrigation detection [11; 19; 20; 21]. [PERSON] et al. [22] were the first to use SSM for irrigation mapping by exploiting the differences between coarse-resolution microwave SSM products (ASCAT, AMSR-E, SMOS, WindSat and ESA CCI) and SSM modeled by a land surface model that does not account for irrigation. Based on a similar approach, [PERSON] et al. [23] assimilated coarse-scale SSM products at 1 km disaggregated from SMOS (Soil Moisture and Ocean Salinity) products into a simple land surface model forced by precipitation only. Irrigated areas were thus detected using the cumulative analysis increment (difference between the analyzed and the background value of SSM) during the season. Some other studies combined both optical and SAR observation [24], in particular since the launch of radar Sentinel-1 (S1) and optical Sentinel-2 (S2) [25; 26; 27]; see also, for instance, the study carried out in Tunisia by [26] that showed a better irrigation mapping accuracy when using SSM derived from S1 than when using S2 data only.