## 4 Results
### Irrigation Events Detection Performance  
The performance of the irrigation dates detection was evaluated using the accuracy metrics (recall, precision, and F-score) across the four reference plots. The irrigation detection approach yielded varying levels of accuracy across different plots (Figure 7). Figure 8: _Cont._  
Figure 7: The accuracy metrics (recall, precision, and F-score) of the detection of real irrigation dates (d\({}_{\text{Re}}\)) over the maize reference plots of Montpellier in 2017 (P1) and Tarbes in 2018 (P2), 2019 (P3), and 2020 (P4): ten d\({}_{\text{Re}}\) over P1, four each over P2 and P3, and three over P4. In P1 in Montpellier, 70% of the irrigation dates were correctly identified (recall), with 88% accuracy among the detected dates (precision). The F-score, which is a combined measure of precision and recall, achieved a satisfactory value of 78%, indicating a reasonably balanced performance in identifying irrigated areas. In P2, the recall was slightly higher at 75%, indicating that 25% of the irrigated areas were not correctly detected. However, the precision was 100%, meaning that all the identified irrigated events were accurate. The associated F-score was 86%, indicating a good overall performance with a trade-off between recall and precision. For P3 and P4, both exhibited a high recall of 100%, indicating that all irrigated events were correctly identified. However, the precision varied between 80% for P3 and 75% for P4, suggesting that 20% to 25% of the detected irrigated areas were false positives. The F-scores were 89% for P3 and 86% for P4, demonstrating a good overall performance with a slightly higher emphasis on recall. These results highlight the strengths of the approach, providing valuable insights for improving the accuracy and reliability of irrigation date detection. The accuracy of the irrigation dates detection was impacted by  
Figure 8: Real irrigation amounts (grey bars), detected irrigation amounts (yellow bars) at each real date of irrigation (dRe), and NDVI values derived from Sentinel-2 optical images with a 15-day time interval interpolation, along with daily precipitation data over the maize-growing season. The data is presented for four different years and locations: (**a**) Montpellier in 2017 (M1); (**b**) Tarbes in 2018 (P2); (**c**) Tarbes in 2019 (P3); (**d**) Tarbes in 2020 (P4). the presence of false detections, primarily attributed to the low accuracy of SSM estimation under dense vegetation covers and/or the SAR revisit time (see the Discussion section). To ensure operational effectiveness, it is important to not only identify irrigation dates but also estimate the irrigation amounts. Therefore, we conducted a comparison between the detected amounts (\(A_{\text{D}}\)) and the actual amounts (\(A_{\text{R}}\)) on the P1, P2, P3, and P4 plots over the irrigation dates detected using the SSM derived from SAR temporal series in the ascending and descending modes. In Figure 8, both the detected dates and irrigation amounts were presented alongside the real field data. The figure illustrates all observed Sentinel-2 NDVI values, categorizing them based on the vegetative or reproductive stages. It also highlights instances of missed irrigation detection, false detections, and correct detections. Within Figure 8, grey and yellow bars correspond to the real and detected irrigation amounts, respectively. Thus, a true irrigation date detection is represented when a yellow bar is positioned over a grey bar for each actual irrigation date. Conversely, a false irrigation detection (false positive) is indicated by a single yellow bar, indicating that our approach detected irrigation on a date that did not correspond with an actual irrigation event. Likewise, an undetected irrigation date (false negative) is represented by a single grey bar, indicating that our approach failed to detect an irrigation date.