## 5 Discussion
### Climatic and Soil Conditions  
Optirrig SSM simulations were crucial in differentiating rainfall and irrigation events. In the present study, the observed SSM changes derived from the S1 data at the plot and grid scales were compared with the simulated SSM changes from Optirrig under rainfall conditions. This comparison provided valuable insights into the expected SSM values, allowing for the accurate interpretation of S1 data and avoiding irrigation misinterpretations. However, Optirrig filter effectiveness may vary depending on specific cases, and the context and limitations should be considered. Indeed, heavy rainfall can also lead to false positive irrigation detection, particularly in situations where there is a moderately developed maize cover that restricts soil evaporation [97]. This effect is particularly pronounced during the early vegetative stage and late reproductive stages of maize growth when the vegetation cover is not dense enough to attenuate the SAR signal (NDVI \(<\) 0.7) [49], and thus, allow for effective monitoring of the underlying SSM. During this period, the moderately dense vegetation canopy acts as a barrier, limiting the evaporation of soil moisture. In this context, if a heavy rainfall event occurs, the soil can become saturated with water, which means that the soil has reached its maximum water-holding capacity and cannot absorb any more moisture. As a result, the soil remains wet even after the rainfall has ceased. When using Optirrig SSM simulations, which estimate SSM levels based on rainfall inputs, simulations may not exhibit significant changes between two consecutive SAR acquisition dates when soil saturation occurs. This is because the simulations account for the limited capacity of the soil to absorb additional moisture beyond saturation. In fact, the detection of irrigation amounts was found to be less efficient for the plots in the humid climate of Tarbes (P2, P3, and P4) compared with the \"semi-arid\" climate of Montpellier (P1). For the P1 plot, the estimation of the seasonal irrigation amounts exhibited a relatively high accuracy, with a mean absolute error percentage (MAE\(\%\)) of 16.4\(\%\). In contrast, for the P2, P3, and P4 plots in the humid climate, a higher MAE\(\%\) of 50\(\%\) was obtained, suggesting a larger discrepancy between the detected and actual amounts of irrigation. This discrepancy was likely due to the challenges posed by the humid climate, where heavy rainfall events can contribute to soil saturation, thereby affecting the accuracy of irrigation detection and amount estimation. The effectiveness of the irrigation detection approach, particularly in estimating irrigation amounts, appears to be influenced by the local climate conditions, with the humid climate introducing additional complexities. Moreover, SSM\({}_{\text{S1}}\) values at the plot scale may remain high due to the water content in the saturated soil, where evaporation and rapid dry-out are restrained. Infact, the S1 SAR signal is sensitive to the presence of water in the soil, resulting in elevated SSM values. However, at the grid scale (10 km \(\times\) 10 km), SSM values calculated over bare soils may show stability or decrease since bare soil areas have higher evaporative capacities compared with plots with developed maize cover. In such situations, the method used for irrigation detection may interpret the observed increase in SSM variation at the plot scale as an indication of irrigation occurrence. In other words, Optirrig simulations for rain-fed conditions may not show substantial SSM changes, and the S1 SSM values at the plot scale may remain high, leading to false irrigation detection. In the study, three false positive irrigation detections were observed after relatively heavy rainfall events, specifically over P1 on 01-Jul (DOY = 182), P3 on 20 August (DOY = 232), and P4 on 19 August (DOY = 232).