## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Application of Agricultural Assimilation** & **References** \\ \hline EPIC & Simulate crop growth, soil erosion, nutrient cycling (Sustainable & [34] \\  & management of agro-ecosystem) & [34] \\ \hline GLAM & Simulated crop growth and yield are affected by global climate change (The & \\  & effects of climate change on crop physiological and ecological processes & [41] \\  & were considered) & \\ \hline HERMES & Simulation of crop growth and water balance (It is especially suitable for & \\  & crop management in the Mediterranean climate region) & [39] \\ \hline STICS & Simulate crop growth, soil moisture, and nutrient dynamics (Suitable for & \\  & wheat, corn, and other crops and different soil types) & [42] \\ \hline SUCROS & Simulate the growth and yield of sugar crops, such as sugarcane (The & \\  & physiological and ecological processes of crops and climatic factors & [43] \\  & were considered) & \\ \hline SWAP & Simulation of water cycle and crop growth in farmland system (Suitable for & [44] \\  & arid and semi-arid areas) & \\ \hline WOFOST & Simulation of crop growth, yield, and soil moisture dynamics (Crop & \\  & physiological and ecological processes and climate change are considered) & [45] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Canada, and other countries are particularly active in the field of agricultural DA research, with fruitful results (see Figure 2b) that may be related to the advantages of agricultural scientific research strength, technological innovation, and policy support in these countries. However, the current research on the DA method in the field of agriculture is still relatively limited, which may be closely related to the consideration of assimilation cost, the difficulty of technology implementation, the complexity and variability of agricultural production itself, and so on. Although some scholars have combed and summarized the progress of the application of DA in agriculture in detail, and achieved a series of important research results [30; 51; 52], it is undeniable that there are still significant gaps in the following aspects:  
* The choice of emerging RS platform DA: in the face of an endless stream of new generation RS data (such as satellite constellation, UAV, new RS platform, high space-time resolution, etc. ), how to make use of the characteristics of multi-source data, consider application scenarios and cost-effectiveness and other factors, select and effectively integrate these data, and further solve the data source problem of DA is an urgent need for the current research. * In-depth understanding of the internal mechanism of DA model: there is a lack of discussion on parameter selection and optimization, uncertainty quantification, multi-model fusion, and fine management of the DA model in existing precision agriculture research, which restricts the further improvement of DA model performance and the expansion of agricultural application. * The applicability of new challenges to DA: under the background of extreme climate change, precision agriculture, and the rise of the agriculture 4.0 digital era, challenges such as the processing of new RS sources of DA, multi-dimensional application scenarios, robust models, and new requirements for high-precision DA have not been fully studied and addressed. Based on the above analysis, this study will focus on the above issues and provide new ideas for the future development of DA in the field of precision agriculture. Through multi  
Figure 2: (**a**) changes in peer-reviewed publications over time, (**b**) the spatial geographical distribution of data assimilation studies. The color depth directly reflects the level of research activities and output in different regions.