## 4 Discussion  
### Challenges Faced by DA in Agricultural Application  
#### 4.1.1 Deep Fusion and Uncertainty Management of Multi-Source Heterogeneous Data  
The issue of assimilation accuracy, to some extent, stems from data challenges, and the integration of multi-source heterogeneous data (such as RS, meteorological data, field observations, drone inspections, model simulations, etc.) offers a potential solution to this challenge [222]. However, the discrepancies in RS data currently used in agriculture are among the primary obstacles limiting the deep fusion of such multi-source and heterogeneous datasets. RS data with varying spatial, temporal, and spectral resolutions, as well as scale differences, when used as inputs to drive CGMs, can steer the evolution of model simulation accuracy and reliability in different directions [223; 224; 225]. Figure 11 vividly illustrates the disparities among different satellite sensors in terms of spectral, spatial, temporal, and radiometric resolution and scale. Generally, for regional-scale applications like regional water-saving irrigation, global climate change monitoring, and earth resource surveys, medium to low-resolution data from sources like MODIS and Sentinel-3 is sufficient [226; 227]. Conversely, for local, real-time scale applications such as environmental monitoring and disaster emergency response, the demand for high precision and real-time data is extremely high, necessitating high-resolution RS data [228; 229]. It is noteworthy that the acquisition and utilization of RS data in actual agricultural applications are not proactive processes and often overlook the significance of the differences in RS data (resolutions, time steps, observation modes, error characteristics, etc. ), thereby increasing the complexity of integrating multiple RS datasets [230; 231]. On the one hand, errors in observational data are inevitable, and even when actual crop growth data are obtained, they often suffer from sparsity in both space and time, potentially leading to insufficient fused information and degradation of data precision and quality [232]. For instance, in large-scale SM monitoring, due to resource constraints, only a limited number of monitoring stations can be established, with considerable distances between them, making data points across the entire farmland appear sparse. Additionally, instrument inaccuracies, environmental factors (like wind direction and cloud thickness), and human operational errors all introduce biases [140; 162]. On the other hand, during data fusion, there is a lack of consideration for how well CGMs adapt to the data, as there are no visual references, leading to the natural absorption of correlation errors introduced by the diversity in RS data. Models may fail to fully represent crop conditions, especially when ground practices and climate impacts are not reliably understood, focusing more on critical parameter inputs and direct replacements. If certain CGMs data are not suitable for the current study area or climatic conditions, they can introduce substantial errors, propagating uncertainties in multi-source integration, which highlights the importance of incorporating a development agenda aimed at refining imperfect models to align with actual crop development, thereby mitigating uncertainties and errors [233; 234]. With the rapid advancement and broad application of precision agriculture assimilation, fully considering and implementing a development agenda to guide precise decision-making, cross-sectoral resource integration, and addressing new scenario challenges is of vital importance. Moreover, data quality control and uncertainty quantification are crucial considerations in the multi-source integration process. The quality of RS data from diverse sources and structures may vary greatly, and if rigorous selection of RS data quality is not performed, the fused outcome could potentially be less accurate than the original datasets [235; 236].