Improving nitrogen (N) use efficiency in maize is essential for increasing productivity and reducing environmental losses. This thesis investigates whether dynamic crop growth metrics—derived from time series of physical height measurements and vegetation indices (VIs)—can enable early-season prediction of N status in maize. In Chapter 1, exponential models were fit to manually measured crop height trajectories from six site-years in New York. Dynamic parameters such as cumulative growth and growth rate were compared to static height for their ability to predict N status prior to side-dress application. Cumulative growth was more stable than growth rate and sometimes outperformed static height, though no model consistently exceeded the R² ≥ 0.6 benchmark. In Chapter 2, VI time series from handheld and UAV-based sensors were analyzed across six site-years in New York and Quebec. Exponential models fit VI trajectories well, and dynamic VI metrics—particularly cumulative growth—were more stable than greening rate or static VI values. VI-based dynamics also correlated with physical crop height, supporting their use as proxies. However, prediction accuracy remained variable by site-year and environment. While both approaches showed promise for early N status diagnosis, their effectiveness was context-dependent. These findings suggest that dynamic modeling of crop growth—whether physical or spectral—can improve the timing and precision of N applications, but further research is needed to enhance consistency and scalability, especially using remote sensing platforms.