## 1 Introduction  
Irrigated agriculture is the largest consumer of fresh water, accounting for a substantial 70% of total freshwater withdrawals [1]. The water demands for irrigated agriculture are anticipated to rise, posing significant challenges to irrigation and freshwater availability,particularly in water-scarce regions like the Mediterranean basin [2]. As areas affected by drought increase, irrigation becomes more and more crucial for achieving optimal crop growth and production [3; 4; 5]. Satellite remote sensing has exhibited considerable effectiveness in the diagnostic and supervisory aspects of irrigation, particularly in the context of mapping and monitoring cropped and irrigated areas [6; 7; 8], either with optical [9; 10; 11; 12] or radar sensors [13; 14; 15; 16; 17]. In detail, the interpretation of several optical vegetation indices, such as the NDVI [12], the normalized difference water index (NDWI) [18], or the greenness index (GI) [19], was demonstrated to be effective in exploring discrepancies in the spectral attributes of crops [20]. Recently, [PERSON] et al. [21] developed a novel methodology to detect irrigation dates and amounts for maize crops in the Occitanie region, France, using leaf area index (LAI) values derived from optical images and the Optirrig crop growth and irrigation model. The approach involved analyzing cloud-free Sentinel-2 (S2) optical images to identify potential irrigation events and selecting the most probable event that minimized the difference between the predicted and observed LAI values derived from S2. The methodology achieved an overall recall of 81.6% for irrigation date detection when evaluated with daily interpolated LAI data. Although the methods described above, which depend on optical satellite observations, have shown promising outcomes in mapping irrigated areas and detecting irrigation events, their effectiveness is constrained in various tropical and temperate regions due to the sensitivity of optical data to weather conditions and cloud cover. Surface soil moisture (SSM) is directly influenced by rain and irrigation practices (time, frequency, and amounts), which makes it relevant information for irrigation detection and monitoring or supervision purposes [22; 23; 24]. By monitoring SSM at the plot scale, farmers and water managers can make informed decisions about irrigation scheduling, optimizing water resources, and maintaining crop health for real-time irrigation monitoring, as well as long-term irrigation practice supervision [25; 26]. Over the past four decades, there has been notable enhancement in the quality of surface soil moisture (SSM) products derived from both active and passive microwave remote sensors [27]. These advancements have greatly improved the ability to estimate and monitor the moisture content of the top layer of soil, typically not exceeding a depth of 10 cm [28]. [PERSON] et al. [29] were the pioneers in utilizing the disparities between low-resolution microwave-based SSM products, such as ASCAT [30], AMSR-2 [31], SMOS (Soil Moisture and Ocean Salinity [32]), AMSR-E [33], WindSat [34], and ESA ECV (ESA's essential climate variable product [34]), and the SSM simulated using a land surface model (Noah) that does not include anthropogenic interventions like irrigation [35]. Similarly, [PERSON] et al. [36] employed a comparable approach by assimilating coarse-scale SSM data derived from SMOS products, disaggregated to a 1 km resolution, into a basic land surface model driven solely by precipitation. In this method, irrigated areas were identified with satisfactory accuracy by examining the cumulative analysis increment, which represents the difference between the analyzed and background SSM values throughout the season. Similarly, [PERSON] et al. [22] employed various coarse-resolution datasets and compared them with a water balance driven by the MERRA-2 model in order to assess the utilization of irrigation water.