## 1 Introduction
The examination conducted at a statewide level yielded divergent findings (with correlation coefficients ranging from 0.36 to 0.8) and notably lower estimations compared with the documented irrigation water withdrawals. However, the estimations of irrigation mentioned above are generally obtained at a monthly time scale and for pixels covering several square kilometers (km\({}^{2}\)), making them inadequate for effective irrigation supervision, both in terms of spatial and temporal resolutions. Therefore, acquiring irrigation information at the plot scale requires the use of high-spatial-resolution SAR (synthetic aperture radar) data, which was found to be especially beneficial for efficient irrigation monitoring at smaller agricultural scales. Hence, the sensitivity of SAR signals to soil and vegetation water could help in the detection of irrigation events, as irrigation gradually enhances the water content of both the soil and vegetation. Extensive literature has consistently shown a direct correlation between the SAR backscattering coefficient (\(\sigma^{0}\)) and the water content of the soil and vegetation [37; 38; 39; 40]. Among various SAR satellite constellations, the Sentinel-1 (S1) SAR constellation (S1A and S1B) stands out as an effective tool for mapping and monitoring large-scale irrigated areas. This is attributed to its unique combination of a high revisit time (6-day interval) and high spatial resolution (pixel spacing of 10 m \(\times\) 10 m). [PERSON] et al. [41] demonstrated that C-band S1 SAR data can effectively detect irrigation activities in irrigated wheat plots with a three-day interval between irrigation and SAR acquisition. Detecting consecutive irrigation events requires a comprehensive multi-temporal dataset due to the dynamic nature of irrigation. In a recent study by [PERSON] et al. [17], a near real-time approach was developed to determine irrigation timing in diverse summer and winter crop fields located in France and Spain. The approach involved a change detection methodology that examined two consecutive S1 observations to detect irrigation activities using the S1 SAR \(\sigma^{0}\) and S2 NDVI. The findings revealed that the proposed method achieved an approximate overall accuracy of 84% in detecting irrigation events that occurred in agricultural plots. In addition, several studies showed the potential of SSM products derived from S1 SAR observations for monitoring and detecting irrigation events over agricultural areas [13; 42; 43; 44] since the SAR signal is sensitive to SSM through the dielectric properties of the soil [45; 46]. [PERSON] et al. [43] examined the potential of the S\({}^{2}\)MP (Sentinel-1/Sentinel-2-derived soil moisture product [47]) in detecting irrigation events at the individual plot level over irrigated maize fields of southwest France. S\({}^{2}\)MP is a soil moisture estimation product that combines Sentinel-1 SAR data and Sentinel-2 optical data for SSM estimation using the neural networks technique [47]. The results demonstrated a high accuracy in detecting irrigation timing, with an F-score ranging from 80% to 83% for all the maize plots examined. The ability to detect irrigation events from SAR data is predominantly influenced by several factors, including the revisit time; radar wavelength (SAR characteristics); and vegetation cover, which encompasses crop type and growth phase [43]. Additionally, the effectiveness of irrigation event detection may be impeded by extended revisit times that do not align with the rapid drying of the soil surface, which occurs within 2-3 days after irrigation, resulting in water content values similar to those observed before irrigation [48]. Moreover, [PERSON] et al. [49] demonstrated limitations in estimating soil moisture over cereal crops and grasslands using (C-band) S1 SAR data that were attributed to the limited signal penetration under well-developed vegetation covers.