## 1 Introduction
Consequently, this highlights the potential advantages of utilizing L-band data (lower frequency, longer wavelength, and enhanced penetration capabilities) for future applications [50]. From a different perspective, relying on the modeling of biophysical processes, crop growth models have emerged as highly valuable tools over the past three decades, enabling the assessment of agronomical needs and traits such as crop water requirements, biomass, and yield, which has made them suitable for crop development and monitoring irrigation [51; 52; 53]. Extensive multidisciplinary efforts in crop modeling have led to the refinement of various dynamic process-based crop models incorporating water balance and evapotranspiration components, such as AquaCrop [54], STICS [55], SAFYE [56], and Optirrig [57]. The latter focuses on the identification of appropriate plot-scale water management strategies while being as parsimonious as possible in the description of plant physiology and soil water budget dynamics. These models were shown to be useful for monitoring water application effects and stress on plant growth, as well as simulating the crop response to irrigation [58; 59; 60; 61; 62]. Consequently, crop models, such as Optirrig, are regarded as powerful tools for defining crop water requirements and optimizing irrigation management strategies based on management practices and soil conditions, particularly soil water balance [63; 64]. Notably, soil water content plays a critical role in Optirrig, as it represents the available water in the soil profile for plant utilization, which is essential for predicting crop development, biomass accumulation, and overall crop yield. The Optirrig crop model incorporates factors such as climatic conditions, soil properties, and management practices (e.g., irrigation, sowing, and harvest dates) to simulate the water balance in the soil [57; 65]. For example, [PERSON] et al. [65] conducted a study on irrigated maize fields to assess the capabilities of the Pilote model (subsequently transformed and enhanced to serve as the agronomic and hydrological core of Optirrig) for crop development and yield prediction. The model demonstrated the capacity to provide satisfactory simulations and predictions of leaf area index (LAI) values, which is a pivotal variable within its conceptual and numerical schemes due to its precise estimations of soil water balance, even in the initial shallow reservoir of 10 cm in depth, throughout the crop's growth cycle. However, the implementation of crop and irrigation models is often limited by the availability of input data [66], which could hamper the accuracy of soil water content simulation in the model and, therefore, the accuracy of crop yield predictions and irrigation scheduling [67]. Consequently, the uncertainties encountered in estimating daily or seasonal model variables, encompassing soil water reserves, crop development, biomass accumulation, and final yield, are frequently attributable to the limited understanding of soil characteristics and crop management practices across extensive geographical extents [68; 69]. To mitigate these uncertainties, several studies have integrated information obtained from remote sensing-derived soil moisture data into crop and soil water models, thereby enhancing agricultural assessments and irrigation monitoring approaches [70; 71; 72; 73]. For instance, [PERSON] et al. [71] integrated ESA CCI SSM at a spatial resolution of 0.25 degrees into the DSSAT model. The aim was to estimate both the surface and root-zone soil moisture and assess the impact of data assimilation on agricultural drought monitoring. The findings revealed a significant improvement in soil moisture estimates following the assimilation of SSM data, which potentially improves the estimation accuracy of crop yields and irrigation requirements [74; 75; 76; 77]. Similarly, [78] conducted a study to assess the impact of integrating in situ and remote sensing soil moisture observations with the APSIM model through sequential data assimilation.