Managing N applications with remotely sensed data or in-field data offers the prospect for improving N use efficiency by adjusting applications to small-scale variability. Passive, multi-spectral airborne imagery and ground based handheld passive reflectance sensors have been shown to be effective tools for determining the N status of corn.  More recently, active reflectance sensors have been developed which could expand our ability to assess the physiological state of corn regardless of natural illumination.  Unfortunately, only a short time span usually exists between the expression of N stress in corn and the last opportunity to apply fertilizer for maintaining optimal yields.  Multi-temporal remotely sensed data were collected over variable N rate plots at the Univ. of MD research farm at Quantico, MD and at the USDA-ARS farm at Beltsville, MD to determine the earliest growth stage at which N stress can be detected.  A comparison of airborne imagery and ground based active and passive multi-spectral reflectance was performed to determine the effectiveness of each technique for detecting N status and predicting final grain yield.  The vegetation indices derived from these data were related to physiological measurements including percent cover, leaf area index, leaf chlorophyll content, and grain yield.  Vegetation indices were found to be most related to percent cover in the early stages of development, crop N status became more strongly correlated at, or after, the corn V-8 growth stage.