Evaluating corn nitrogen variability via remote-sensed data

Transformations and losses of nitrogen (N) throughout the growing season can be costly. Methods in place to improve N management and to facilitate split N applications during the growing season can be time consuming and logistically difficult. Remote sensing (RS) may be a method to rapidly assess temporal changes in crop N status and to promote more efficient N management. This study was designed to evaluate the ability of three different RS platforms to predict N variabilin corn (Zea mays L.) leaves during vegetative and early reproductive growth stages. Plots (15 x 15 m) were established in the Coastal Plain (CP) and in the Appalachian Plateau (AP) physiographic regions each spring from 2000 to 2002 in a completely randomized design. Treatments consisted of four N rates (0, 56, 112, and 168 kg N ha-1) applied as ammonium nitrate (NH4NO3) replicated four times. Spectral measurements were acquired via spectroradiometer (lambda = 350-1050 nm), Airborne Terrestrial Applications Sensor (ATLAS) (lambda = 400-12,500 nm), and the IKONOS satellite (lambda = 450-900 nm). Spectroradiometer data were collected on a biweekly basis from V4 through R1. Due to the nature of satellite and aircraft acquisitions, these data were acquired per availability. Chlorophyll meter (SPAD) and tissue N were collected as ancillary data, along with each RS acquisition. Results showed vegetation indices derived from hand-held spectroradiometer measurements as early as V6-V8 were linearly related to yield and tissue N concentration. The ATLAS data were correlated with tissue N at the AP site during the V6 stage (r2 = 0.66), but no significant relationships were observed at the CP site. No significant relationships were observed between plant N and IKONOS imagery. By using a combination of the greenness vegetation index and the normalized difference vegetation index, RS data acquired via ATLAS and the spectroradiometer could be used to evaluate tissue N variability and to estimate corn yield variability given ideal growing conditions.