## 4 Discussion
### Challenges Faced by DA in Agricultural Application
#### 4.1.3 From Univariate Assimilation to Multivariable and Multiscale Assimilation  
Current DA primarily focuses on the assimilation of individual key variables such as LAI and NDVI, largely due to mismatches in scales between observations and models [267]. Satellite or sensor observations typically provide information at the pixel level, reflecting vegetation status over larger spatial scales and encapsulating complex, mixed ground information. Conversely, models operate based on simplified assumptions and interpret single variables, struggling to precisely capture all relevant biophysical processes when dealing with interconnected micro-scale variables like SM, nutrient content, and pest pressure. These variables are not directly measurable by RS, and model simplifications may not adequately represent their collective influence. Consequently, DA often prioritizes parameters that are easily extracted and have a significant impact on model predictions, overlooking the synergistic effects of multiple interrelated variables during crop growth. While this approach may suffice for broader tasks such as land cover classification, vegetation monitoring, and crop area estimation on a macroscopic scale [268; 269], it can prove disastrous for precision operations, leading to intensified drought and flooding, environmental pollution, yield impacts, delayed pest management, and economic losses [270; 271; 272]. Although simpler to implement, this approach lacks versatility, especially in refined agricultural practices.  
A promising solution lies in developing multi-variable, multi-scale integrated assimilation techniques to tackle key issues related to data fusion, model coupling, and uncertainty handling. This necessitates addressing scale transformation problems across different spatial and temporal dimensions, particularly when non-linear transformations and landscape heterogeneity are involved. Adjusting the spatial scale of agricultural landscapes from satellite RS to field scale typically involves averaging satellite (LAI/NDVI) data over fields to represent overall vegetation health [273; 274], which may not adequately capture landscape variability [275]. More sophisticated downscaling techniques, such as ML-based methods trained to predict high-resolution parameter values, can be employed. Researchers have linked high-resolution ground measurements with RS (LAI/NDVI) to establish relationships with field-scale crop growth parameters, subsequently applied to low-resolution satellite data to estimate crop conditions at the field scale [276]. Furthermore, CNNs have been used to enhance the resolution of satellite RS images, improving the estimation accuracy of vegetation parameters at the field scale [277].  
Adjusting for the time scale requires careful consideration, especially when converting daily data to decadal or monthly averages to match model time steps. Simple averaging or statistical downscaling may not suffice given the inherent nonlinear dynamics of time series data [176; 278]. Adopting assimilation strategies that couple space and time and incorporating dynamic models to capture nonlinear temporal dynamics presents a promising avenue. For instance, in predicting pest outbreaks, spatiotemporal assimilation techniques can be designed to integrate forecasting systems seamlessly, enhancing the accuracy and timeliness of disease warnings [279]. Notably, future research may concentrate on dynamic windowing and adaptive filtering techniques [280], as well as ML-based multi-scale assimilation models [281; 282], marking a focal point for advancing multi-variable, multi-scale integrated assimilation.