This study develops and assesses two methods for estimating median surface grain sizes using digital image processing from centimeter-resolution airborne imagery. Digital images with ground resolutions of 3 cm and 10 cm were combined with field calibration measurements to establish predictive relationships for grain size as a function of both local image texture and local image semivariance. Independently acquired grain size data were then used to assess the algorithm performance. Results showed that for the 3 cm imagery both local image semivariance and texture are highly sensitive to median grain size, with semivariance being a better predictor than image texture. However, in the case of 10 cm imagery, sensitivity of image semivariance and texture to grain size was poor, and this scale of imagery was found to be unsuitable for grain size estimation. This study therefore demonstrates that local image properties in very high resolution digital imagery allow for automated grain size measurement using image processing and remote sensing methods.