## 4 Discussion
### The Future Development Prospect of DA  
#### 4.2.1 Intelligent and Integrated DA  
DA applications in agriculture are trending towards greater intelligence and precision, with efficiency and accuracy continually enhanced by the rapid development of multi-sensor integration, cloud computing, Internet of Things (IoT), and artificial intelligence technologies--a consensus among agricultural practitioners [283; 284]. In the context of smart greenhouse and field management studies, IoT technology facilitates real-time monitoring and collection of microscale data, including SM, temperature, crop growth status, and soil chemical properties, which are then efficiently consolidated via cloud platforms [285]. The Copernicus Sentinel satellite constellation, offering high-resolution optical and thermal sensing data, combined with high-resolution RS and in-field sensor data revolutionizes Earth observation, enabling agricultural managers to adjust irrigation and fertilization strategies in real-time and with precision through advanced DA algorithms processed in parallel on cloud servers. This results in enhanced accuracy of crop yield forecasts [286; 287]. In addition, the assimilation of spatial scale and time dimension and the tradeoff and fusion application of their integration methods are important development directions in the field of data assimilation in the future [288; 289; 290]. For example, we could explore a more adaptive spatial scale assimilation method, which can deal with the differences of data characteristics and model requirements in different regions and different environmental conditions. In addition, the introduction of time series analysis and sliding window technology can realize the real-time tracking and assimilation of the dynamic process and improve the ability of the model to support short-term prediction and real-time decision-making. Simultaneously, AI technologies have transformative potential, optimizing model parameter estimation and reducing response times through historical data analysis, thereby enhancing agricultural adaptability and resilience under complex climatic and environmental changes [291]. Nevertheless, the integration of smart technologies with precision DA in agriculture must navigate the unique complexities and variability of agricultural systems [292]. For example, data limitations, such as heterogeneity, singularity, scale differences, and uncertainties, often pose significant barriers to progress [74]. Yet technological advancements, like the Planet-Scope, Planet Labs Dove satellite constellations, and UAVs, facilitate the integration of multi-source data, overcoming challenges related to data uniformity and scale [86; 87]. High-resolution satellites like Landsat-9 significantly contribute to discerning subtle variations in agricultural landscapes, complementing ground observations [78]. For complex CGMs, whether simulating crop development, processing data, or tracking growth stages dynamically, DA incurs significant costs, especially at larger spatial and temporal resolutions for regional or national scale simulations. The trade-off between model accuracy and complexity is often skewed towards simplicity for predictive power, neglecting modular design. CGMs typically treat simulation units as independent entities, allowing different modules to be assigned to separate units for parallel processing in cloud servers, a strategy facilitated by advances in CUDA, distributed storage, and multi-GPU systems [293]. Considering computational time and cost, Genetic Algorithm-based Task Scheduling (GA-TP), GPU clusters, and computer clusters, alongside AI technologies, are leveraged for their ability to distribute computational loads and expedite processing [294; 295]. Large-scale parallel infrastructures like Google Earth Engine (GEE) offer a platform for redesigning DA algorithms with its immense computational power and data storage, presenting new possibilities [296].