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