Using high spatial resolution multispectral data to classify corn and soybean crops

Digital images of a corn and soybean site in Ohio were acquired several tim es during the growing season using a multispectral scanner mounted on an ai rcraft. The goal of this study was to evaluate the use of this high spatial resolution (1-m) data to identify corn and soybean crops at various growth stages. Maximum distinction between corn and soybeans was achieved using t he near-infrared bands when the crops were mature, while the visible bands were more useful when the soybeans were senescing. Spectral class differenc es were related to leaf nitrogen, soil water content, soil organic matter, and plant biomass. An approach is presented for identifying corn and soybea ns crops where little or no reference data are available. The approach is b ased on the red and near-infrared bands and using the Simple Vegetation Ind ex or the Normalized Difference Vegetation Index.