Crop model parameters are often difficult to specify and more complex models are challenging to calibrate [27; 28]. Consequently, models are often over-fitted for specific sites, which presents complications when scaling up to provide regional estimates [29; 30]. The introduction of the N cycle, with links to N management and soil-water dynamics, is one of the most common factors that result in an increase in crop model complexity and the number of required inputs [25]. Research in [PERSON] et al. [31] includes an inter-comparison of 11 widely used crop models for predicting yield responses to different N application rates. Most of these models used a detailed representation of the components of the N cycle, which included processes effecting N mineralization, N movement, N demands, and the responses of N stress on growth and phenology. Despite the high level of detail, generally these models did not accurately reproduce yield responses to N applications, with the majority of the models underestimating yields by greater than 1 t ha\({}^{-1}\). In this research, we merge observations of crop canopy development (e.g., using EO data) with the predictive capabilities of an intermediate complexity model, which simulates interactions of leaf N traits, climate and yield. We hypothesize that this modelling approach can provide reliable estimates of plant leaf N traits attributed to management. Specifically, the modelling of yield over a growing season can be summarized:  
\[\text{Yield}=f(LAI,Leaf\ N,\ climate), \tag{1}\]  
where yield is dependent on the interception and absorption of solar radiation (i.e., _climate_) by available leaf area (i.e., _LAI_) and its carboxylation capacity (i.e., _leaf N_). Essential climate drivers include solar energy, required for photosynthetic reactions, and temperature, which effects the rates of crop development and photosynthesis. Prior to harvest the best proxy for yield is LAI, which is the largest biomass pool and the most readily observable, with many LAI products generated from satellite data. Research has also demonstrated improved crop model yield simulations when assimilating LAI observations [32; 33; 34]. Thus, the LAI at a time \(t\) should be explained by the chronological development of LAI and leaf N, along with the climate during the preceding period. Since LAI is only one component of the yield equation, its growth must also be consistent with the evolution of the crop's developmental stage up to time \(t\):  
\[\text{Yield}=f(LAI,Leaf\ N,\ climate,\ developmental\ stage), \tag{2}\]  
There are reliable models of developmental stage linked to climate data, and both LAI and climate can be determined from EO and meteorological re-analyses data [35; 36]. Thus, for a given time-step, leaf N is the only unknown, and can therefore be inferred based on the observed LAI and climate data, and the model of developmental stage. A key question is what information content does LAI and climate data provide in such an inversion? and what is the relative importance of each of these constraints? Here we test whether a model-data fusion (MDF) approach can combine observations of crop canopy states (LAI) and climate with process modelling to estimate yield and leaf N traits robustly. If leaf N can be routinely inferred from EO data and modelling, we can better understand controls on variation in crop leaf N content and yield in response to N-application. Robust crop model evaluations involve testing simulations under a wide range of circumstances. For model calibration and validation, we therefore use comprehensive multi-temporal measurements acquired during two seasons of experimental winter wheat N trial plots in Scotland spanning a range of N treatment levels: from 0 to 200 kg N ha\({}^{-1}\). Field data included non-destructive measurements of LAI along with C and N content data from some destructive samples.