## 5 Discussion
### Uncertainties Associated with Optirrig's Simulations  
In a broader context, the SSM simulations in Optirrig are subject to inherent uncertainties, which can impact the accuracy of irrigation detection. These uncertainties arise from several factors, including the variability in input parameters and the temporal dynamics of vegetation [99; 100]. In fact, the accuracy of SSM simulations depends on the availability and quality of input parameters, such as soil properties, vegetation characteristics, and meteorological data [101]. However, these parameters can vary spatially and temporally, leading to uncertainties in the simulations [102]. Moreover, the temporal dynamics of vegetation, including growth stages, leaf area index, and canopy density, can also introduce uncertainties in SSM simulations. Vegetation plays a crucial role in the water balance of the soil-plant-atmosphere system, as it affects evapotranspiration rates and soil moisture depletion in crop models [103], such as Optirrig. However, accurately representing the dynamics of vegetation within the model is challenging. Changes in vegetation characteristics over time can lead to variations in evapotranspiration rates and water uptake by plants, impacting the simulated SSM values [104] and potentially affecting the detection of irrigation events. To mitigate these uncertainties, there is growing interest in incorporating multiple external and independent data, such as radar and optical data, in various irrigation mapping, detection, and crop monitoring approaches [105; 106]. In a recent study, [PERSON] et al. [107] demonstrated the potential of using remotely sensed vegetation and soil moisture observations (MODIS LAI and SMAP data) to constrain irrigation estimation in the Noah-MP land surface model. Results have shown that incorporating these constraints reduces model sensitivity to irrigation parameterization, improves characterization of irrigation patterns and their effects on evapotranspiration and soil moisture, and corrects for vegetation conditions and irrigation timing. This approach highlights the importance of utilizing remotely sensed data for irrigation detection and provides a less engineered method to constrain irrigation modeling uncertainty. The integration of several remote sensing data sources aims to inform crop models and reduce uncertainties associated with irrigation estimation. In the present case, incorporating both radar data for soil surface moisture estimates and optical data, such as LAI observations, into the inversion process would likely yield substantial benefits. By combining these data, it becomes possible to constrain both the water balance and crop growth schemes of Optirrig. This approach enhances the accuracy and reliability of the crop model, enabling improved detection of irrigation events and more precise estimation of crop water requirements. For instance, [PERSON] et al. [106] developed and tested a methodology using Sentinel-1 and Sentinel-2 satellite observations to monitor the spatiotemporal variations in irrigated areas in the Kudalier catchment, Telangana State, South India. The methodology employed a machine learning algorithm and achieved high accuracy by combining radar and optical data. The results showed the variability of irrigated areas across different seasons and highlighted the potential of this approach for near-real-time monitoring in South India. These findings contribute to calibrating agro-hydrological models and assessing the impact of agriculture on water resources. Incorporating biophysical data, such as LAI, from high-resolution optical images, like those from Sentinel-2, in conjunction with SSM SAR estimations can greatly benefit the constraint of Optirrig simulations. This combination of data sources enhances the monitoring of irrigation practices by providing complementary information on vegetation dynamics and soil moisture levels. By integrating LAI and SSM data, Optirrig can achieve improved accuracy and reliability in detecting and monitoring irrigation practices.