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Hyperspectral remote sensing imagery was collected over a soybean field in central Illinois in mid-June 2001 before canopy closure. Estimates of percent vegetation cover were generated through the processing of RGB (red, green, blue) digital images collected on the ground with an automated crop mapping system (Tang, 2002). A comparative study was completed to test the ability of broad-band, narrow-band, and derivative-based vegetation indices to predict percent crop cover at levels less than 70%. Narrow-band and derivative-based indices utilizing the finer spectral detail of hyperspectral data performed better than the older broad-band indices developed for use with multispectral data. Specifically, second derivative indices measuring the curvature in the green region and longer wavelength red region performed well. Also, a few narrow-band indices using optimized wavelength ranges in the blue, green, red, and NIR regions performed well. The performance of all indices was shown to suffer over areas of brighter soil background, and the use of a narrow-band ratio index that did not require NIR reflectance values performed best in this case.