DISCRIMINANT ANALYSIS OF HYPERSPECTRAL DATA FOR ASSESSING WATER AND NITROGEN STRESSES IN CORN

The development and implementation of both economically and environmentally sustainable precision crop management systems can be greatly enhanced through the use of remote sensing. In this study, the potential of narrow-waveband hyperspectral observations in the discrimination of nitrogen and water stresses in corn (Zea mays L.) was investigated. A field experiment was conducted in the summer of 2002 at the Macdonald Research Farm, McGill University, Ste-Anne-de-Bellevue, Quebec, Canada. Corn was grown in forty 9.0 × 10.0 m test plots laid out in a split-plot design with irrigation (non-irrigated, irrigated) as the main treatment and nitrogen fertilizer application rate (50, 100, 150, 200, and 250 kg ha-1) as the sub-treatment. Hyperspectral measurements in 2151 wavebands (350 to 2500 nm) were made with a field spectroradiometer during the entire growing season. Using a stepwise procedure, the most effective wavebands capable of discriminating treatment effects were selected. By applying a discrimination procedure with a well-chosen subset of the selected wavebands, treatments were correctly classified with more than 95% accuracy. Specific narrow wavebands, from different portions of the spectrum, allowed the discrimination of plots differing in their irrigation and nitrogen treatments. This study supports past work suggesting that greater spectral resolution should lead to more consistent relationships between the spectral data and different crop status indicators.