Technical Abstract: Vegetation indices derived from remotely sensed imagery are commonly used to estimate crop yields. Spectral unmixing techniques provide an alternative approach to quantifying crop canopy abundance within each pixel of an image and have the potential for mapping crop yield variability. The objective of this study was to apply linear spectral unmixing techniques to airborne hyperspectral imagery for estimating crop yield variability. Airborne hyperspectral imagery and yield monitor data collected from two grain sorghum fields were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the hyperspectral imagery with grain sorghum plants and bare soil as endmembers. A pair of pure plant and soil spectra derived from each image and another pair of ground-measured reflectance plant and soil spectra were used to generate unconstrained and constrained soil and plant abundances. To examine how variations in endmember spectra affect the estimation of abundances and correlation coefficients between yield and abundances, 45 additional pairs of soil and plant endmember spectra were used to generate unconstrained and constrained plant and soil abundance images for each field. Statistical analysis showed that yield was positively related to plant abundance and negatively related to soil abundance. Unconstrained plant abundance had essentially the same r-values with yield among all 47 endmember pairs, while unconstrained soil abundance and constrained plant and soil abundances had r-values that were sensitive to the choice of endmember spectra. For comparison, all 5,151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Results showed that the best plant and soil abundances provided higher r-values than 96.3% and 99.9% of all the NDVIs for fields 1 and 2, respectively. Since plant abundance could better represent yield variability than most narrow-band NDVIs, it can be used as a relative yield map especially when yield data are not available. These results indicate that spectral unmixing applied to hyperspectral imagery can be a useful tool for mapping crop yield variability.