Suitability of Remote Sensing Techniques for the Detection of Nitrogen Stress in Corn.

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 facilitate more frequent sampling of crops due to their ability to collect reflectance measurements under variety of sky conditions.  This improves the ability to investigate the relationship between a plants growth stage, the expression of nitrogen stress, and the potential of a sensor to detect that stress.  A multi-year assessment of the potential for remote sensing instrumentation to discern N stress in corn has been conducted in Maryland from 2004 to 2010.  Aerial hyperspectral imagery, ground based passive sensor reflectance data, and ground based active sensor reflectance data were collected.  Additionally, biophysical parameters including plant height, LAI, leaf chlorophyll content, development stage, were acquired weekly from growth stages V6 to R1.  Temporal exhibition of N stress varied from year to year due to residual soil N and meteorological variables (rainfall and temperature). Remote Sensing instrument derived vegetation indices correlation coefficients were strongest with percent cover and LAI at early growth stages. Vegetation indices incorporating red edge bands showed improved correlation with leaf chlorophyll variability when compared with indices that did not utilize the red edge. Correlations between remotely sensed vegetation indices and leaf chlorophyll content typically increased as the corn developed, and N-stress intensified.   Significant correlation between leaf chlorophyll content and remote sensing derived vegetation indices typically occurred at, or after, a growth stage of V8.