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Vegetation indices derived from multispectral imagery are commonly used to extract crop growth and yield information. Spectral unmixing techniques provide an alternative approach to quantifying crop canopy abundance within each pixel and have the potential for mapping crop yield variability. The objective of this study was to apply linear spectral unmixing techniques to airborne multispectral imagery for estimating grain sorghum yield variability. Five time-sequential airborne multispectral images and yield monitor data collected from a grain sorghum field were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the images to generate crop plant and soil abundances for each image and all 26 multi-image combinations of the five images. Yield was related to spectral abundances and significant correlations were found between yield and abundances. For comparison, yield was also related to the normalized difference vegetation index (NDVI) and the green NDVI (GNDVI). Results showed that although unconstrained plant abundance didnt provide as good correlations with yield as GNDVI for three of the five images, it had better correlations with yield than NDVI for each image. Moreover, unconstrained plant abundance provided better overall correlations with yield than constrained abundances, and the unconstrained model was not as sensitive to the variation in endmember spectra as the constrained model. Multi-image combinations improved the correlations with yield and the best three-image combination resulted in the highest overall r2-value (0.813) between yield and unconstrained plant abundance. These results indicate that linear spectral unmixing techniques can be a useful tool for quantifying crop canopy cover and mapping crop yield.