## 1 Introduction
The evaluation was conducted across five experimental sites in the US Midwest, focusing on the improvement in model predictions for downstream state variables. The results demonstrated that assimilating remote sensing soil moisture data led to enhanced crop yield estimates, with a median reduction of 17.2% in RMSE compared with the model predictions. Conversely, recent studies assessed the potential of incorporating SSM derived from high-spatial- and -temporal-resolution Sentinel-1 data into simple land surface and water budget models for irrigation events detection [42; 44]. For instance, [PERSON] et al. [44] conducted a study to introduce and assess a novel method for predicting irrigation timing and amounts at the field scale. The approach involved incorporating S1 SSM data into a simple water budget model, namely, FAO-56 [79]. The results demonstrated that both the timing of irrigation and the amount of water used could be accurately determined if a priori information about irrigation techniques is collected. However, land surface and water budget models often rely on observations or empirical functions to simulate vegetation effects and lack a mechanistic representation [80; 81]. They also tend to overlook the impact of human activities, such as irrigation, on soil moisture changes. On the other hand, crop models have the capability to explicitly capture the entire growth process and simulate interactions between the root-zone soil moisture and crop water uptake. Therefore, crop models, when combined with agronomic parameters and soil information derived from remote sensing, can effectively monitor crop yield and irrigation [80; 82; 83]. The linkage between crop models and remote sensing observations for detecting irrigation is not commonly explored, and there is a dearth of evaluations regarding the impact of integrating soil moisture estimates into crop modeling, among others for irrigation monitoring or supervision. Consequently, this study aimed at incorporating SSM observations derived from the S\({}^{2}\)MP product [47] into Optirrig for irrigation dates and amount detection. In this study, the Optirrig model [57; 84] was used for simulating, analyzing, and optimizing irrigation scenarios. The specific objective assigned to Optirrig was to identify the actual sequence of irrigation events (including dates and amounts) for multiple maize fields under irrigation in the Occitanie region of Southeast France. By simulating the dynamics of soil water content, Optirrig could assess the impact of different irrigation management practices and/or water stress on the SSM. The method aimed at retrieving the irrigation that has probably occurred, leading to changes in the SSM between two SAR image acquisition dates. The approach involved detecting changes in the SSM values derived from the S\({}^{2}\)MP (SSM\({}_{\text{S1}}\)) between the t(t) date of the current SAR image and the t(t) date of the previous image. The main objective was, therefore, to determine the irrigation (date and amount) that could have caused this variation in SSM. This was done through the simulation of the SSM using Optirrig for various irrigation combinations (dates and doses) injected between t(i) and t(t). The irrigation combination that resulted in a similar change in SSM as observed in the SSM\({}_{\text{S1}}\) was considered the most likely one that occurred. It is important to note that in this study, the changes in SSM values were examined by calculating the rate of change between t(i) and t(t). By examining these rates of change, we could assess the similarity or disparity between the observed (SSM\({}_{\text{S1}}\)) and simulated SSM variations in Optirrig, providing insights into retrieving the most probable irrigation date and amount. First, Sentinel-1, Sentinel-2, weather, and irrigation data were collected and then SSM\({}_{\text{S1}}\) values were estimated over maize plots using S\({}^{2}\)MP.