## 6 Conclusions  
This study aimed at the detection of irrigation dates and amounts at the plot level by integrating Sentinel-1 surface soil moisture (SSM\({}_{\rm S1}\)) data into Optirrig using an inversion technique, which fits in the idea of providing methods for the supervision of irrigated perimeters. The methodology involved comparing the change in SSM\({}_{\rm S1}\) between two SAR acquisition dates with the change in SSM simulated by Optirrig for various combinations of irrigation dates and amounts. Two filters were applied to remove the ambiguity between irrigation events and rainfall, and thus, determine the occurrence or not of a potential irrigation event. These filters relied on comparing the change in the SSM at plot scale to the change in SSM at grid scale (10 km \(\times\) 10 km) and in the SSM simulated by Optirrig under the effect of the rainfall alone. The detected irrigation date with the shortest time difference from the real irrigation event date was considered the detected event. To assess the accuracy and robustness, the method was tested against documented situations in four experimental maize plots located in the Occitanie Region of southern France (near Montpellier and Tarbes). This testing helped to evaluate the method's applicability for operational purposes, such as supervising irrigated territories, and provided insights into its strengths, limitations, and potential improvements. The performance of the inversion process in retrieving correct irrigation dates between two successive Sentinel-1 images was evaluated using three metrics: recall, precision, and F-score. These metrics assess the accuracy based on false negative (missed irrigation detection), false positive (incorrectly detected irrigation), and true positive detections. The evaluation of the inversion process using real irrigation data yielded promising results. The recall, which represents the ratio of true positive detections to the sum of the true positives and false negatives, was determined to be 86.2%. This indicates that the inversion process successfully captured a significant portion of the irrigation events. Similarly, the precision, which denotes the ratio of true positive detections to the sum of the true positives and false positives, was calculated to be 85.7%. This metric demonstrates the accuracy of the inversion process in correctly identifying irrigation events while minimizing false detections. Furthermore, the assessment of seasonal irrigation amounts estimation performance for each plot revealed variations based on the climate conditions. The efficiency of irrigation amount detection proved to be lower for the plots situated in the humid climate of Tarbes in comparison with the \"semi-arid\" climate of Montpellier. The estimation of seasonal irrigation quantities for the P1 plot demonstrated relatively accurate results. However, for the plots in Tarbes, a notably higher mean absolute error percentage (MAE%) was recorded, indicating a more significant disparity between the detected and actual irrigation amounts. A comprehensive and meticulous analysis of the results provided valuable insights into the weaknesses and instances of failure in detecting irrigation dates and amounts. The examination of missed and false irrigation detection brought to light the primary limitations of the method, which could be attributed to two key factors. First, the characteristics of the Sentinel-1 constellation, including its 6-day revisit time and the challenges associated with signal penetration in dense maize cover, were identified as contributing to the limitations. Additionally, it was found that the parameterization of Optirrig for SSM simulations did not precisely align with the irrigation practices and decision rules followed by farmers, further impacting the accuracy of the method. The incorporation of external information and constraints to enhance the inversion process represents a valuable perspective for future practical implementations of the method, both at specific plots and across larger irrigated areas.