## 4 Discussion
### Challenges Faced by DA in Agricultural Application
#### 4.1.2 Refinement and Dynamic Updating of Complex Crop Growth Models  
Current CGMs often face limitations when simulating complex nonlinear physicochemical processes, ultimately due to incomplete understanding of crop growth mechanisms and constraints imposed by data quality and availability. On the one hand, models frequently fail to accurately replicate crop growth processes, which are influenced by various factors such as soil properties, water supply, nutrient uptake, light conditions, and pest infestations [250; 251]. On the other hand, studies may lack sufficient and precise input of model parameters, encounter difficulties in measurement, or be hampered by transmission constraints [252]. The input parameters themselves are inherently uncertain and are usually defined within ranges based on prior knowledge and historical calibrations. There is ongoing debate about whether the focus should be on tracing the sources of uncertainty or on qualitatively analyzing the impact of these uncertainties on DA. Regardless of the emphasis, Bayesian frameworks offer effective means for quantifying both the sources of uncertainty and their impacts, providing not only optimal parameter estimates but also a standardized method to evaluate the magnitude and variability of uncertainties [253]. However, the use of advanced sampling techniques like MCMC often requires substantial computational resources and prolonged run times, which remains a topic of discussion [254]. By contrast, the integration of Gibbs sampling with Hierarchical Bayesian DA is seen as an ideal strategy for addressing complexities in model uncertainties [213], leveraging Gibbs sampling's efficiency in univariate conditional probability distributions and the HBA's capacity to handle multilevel uncertainties, although considerations for computational resources in agricultural contexts remain crucial. Model refinement and dynamic updating are enabled by deepening our understanding of physical processes, such as precisely modeling changes in SM, evaporation, infiltration, and crop water uptake in fields, which are vital for realistic representations of crop growth environments. Recent studies highlight the significant benefits of enhanced grid resolution in capturing spatial heterogeneity in farmland, facilitating a more nuanced understanding of crop growth distribution and dynamics [255]. These advancements bring simulations closer to actual crop growth. Furthermore, refined parameter estimation is another advantage, exemplified by improvements to the YOLOv5s algorithm that reduce errors from parameter uncertainties [256]. High-resolution observations and advanced inversion algorithms allow for uncovering local structures and nonlinear relationships in the parameter space, with dynamic updates and adjustments to parameter estimates directly influencing outcomes as new observations are incorporated [257]. Studies have shown that dynamically estimating observation error covariances is more accurate than traditional methods for quantifying observation uncertainties, and, where necessary, assigning weights can help mitigate the impact of observational errors to some extent [258]. These advancements all revolve around quantitative and qualitative analyses of parameter uncertainties to enhance CGMs simulation accuracy; exploring ensemble methods that combine refined models rather than relying on a single optimal model leverages the strengths of multiple models and mitigates biases [259]. Inspired by online learning in ML, frameworks that dynamically adjust model parameters based on new observations are being designed to improve models' timeliness and adaptability to complex scenarios [260; 261]. However, it must be acknowledged that model refinement and dynamic updating can increase the complexity of model structures, leading to more state variables and parameters.