## 4 Discussion
### The Future Development Prospect of DA
#### 4.2.2 DA Applicability of Decision Support for Precision Agriculture  
DA, serving as a crucial link between observational data and CGMs, has been extensively demonstrated through cutting-edge research to hold immense potential and application value in supporting precision agriculture decision-making, such as precision irrigation, detailed yield estimation, targeted monitoring, and precision pest control [83; 223; 301]. Despite these advancements, there remain gaps to bridge before DA fully aligns with the practical requirements of precision agriculture. First and foremost, the practice of precision agriculture imposes stricter demands on the quality and integration of observational data, likely tied to the necessity for high-quality agricultural decisions. Persistent challenges include cloud coverage, sensor errors, resolution disparities, and scale integration. Researchers have thus focused on constructing advanced data fusion frameworks, such as those based on Bayesian theory, designed to simultaneously address complex nonlinear issues arising during the fusion process. These frameworks typically incorporate multi-source RS platforms like LiDAR, SAR, hyperspectral imagery, and high-resolution data from drones to enhance spatial-temporal resolution and data continuity [302; 303]. The EU Horizon 2020 project \"SEN4 CAP\" exemplifies this approach. Additionally, for cloud detection and gap filling, ML algorithms, notably CNNs and transfer learning techniques, have emerged as promising solutions, validated by tools like FMask on GEE [304] and the Sen2 Cor cloud detection algorithm developed by the European Space Agency (ESA) [305].  
Second, the suitability of CGMs in precision agriculture DA necessitates addressing issues related to model optimization and quantification of parameter uncertainties. Approaches range from rule-based expert systems to ML-driven black-box simplifications of models, such as the simplified version of FAO's AquaCrop model, AquaCrop-OS [223], and the USDA ARS's Excel-based simplified EPIC model, which greatly facilitate farmer and consultant use [306]. Deeper explorations might involve modular and component-based methodologies, with initiatives like the US DOE's Agricultural Model Intercomparison and Improvement Project (AgMIP) [307] indicating a promising path forward. The discourse on parameter uncertainty quantification revolves around MCMC and Bayesian parameter estimation under a Bayesian framework, with future developments potentially leaning towards data-driven adaptive CGMs.  
Lastly, the choice and adaptability of DA algorithms as core components of precision agriculture decision support systems are critical to ensuring the full exploitation of DA in precision agriculture scenarios. Different DA methods, including forcing methods, 4D-Var, EnKF, PF, and HBM, each have distinct applicability and performance characteristics in precision agriculture. Forcing methods are primarily suited for parameter optimization and observational correction [308], whereas variational and Bayesian assimilation methods are better equipped for large-scale, global, or complex uncertainty handling tasks, such as refined yield prediction [309; 310]. HBM particularly excels in integrating multi-source RS data and assessing model uncertainties [119]. Conversely, EnKF and PF show advantages in addressing medium-sized, localized issues, such as precision irrigation decisions, SM forecasting, and decision management [173; 311]. Studies also propose hybrid assimilation strategies, e.g., EnKF-Variational assimilation and EnKF-SSPE [312; 313], and emphasize the potential of leveraging GPU, cloud computing, and parallel platforms like GEE to enhance assimilation efficiency through large-scale parallel computations, a highly prospective research direction [314].