Image analysis using proximal sensors can help accelerate the selection process in plant breeding and improve the breeding efficiency. However, the accuracies of extracted phenotypic traits, especially those that require image classification, are affected by the pixel size in images. Ground coverage (GC), the ratio of projected to ground vegetation area to total land area, is a simple and important trait to monitor crop growth and development and is often captured by visual-spectrum cameras on multiple platforms from ground-based vehicles to satellites. In this study, we used GC as an example trait and explored its dependency on pixel size. In developing new spring wheat varieties, breeders often aim for rapid GC estimation, which is challenging especially when coverage is low (<25%) in a species with thin leaves (ranging from 2 to 15 mm across). In a wheat trial comprising 28 treatments, high-resolution images were manually taken at ca. 1 m above canopies on seven occasions from emergence to flowering. Using a cubic interpolation algorithm, the original images with small pixel size were degraded into coarse images with large pixel size (from 0.1 to 5.0 cm per pixel, 26 extra levels in total) to mimic the image acquisition at different flight heights of an unmanned aerial vehicle (UAV) based platform. A machine learning based classification model was used to classify pixels of the original images and the corresponding degraded images into either vegetation and background classes, and then computed their GCs. GCs of original images were referred as reference values to their corresponding degraded images. As pixel size increased, GC of the degraded images tended to be underestimated when reference GC was less than about 50% and overestimated for GC > 50%. The greatest errors (about 30%) were observed when reference GCs were around 30% and 70%. Meanwhile, the largest pixel sizes to distinguish between two treatments depended on the difference between GCs of the two treatments and were rapidly increased when differences were greater than the specific values at given significance levels (i.e. about 10%, 8% and 6% for P < 0.01, 0.05 and 0.1, respectively). For wheat, small pixel size (e.g. <0.1 cm) is always required to accurately estimate ground coverage when the most practical flight height is about 20 to 30 m at present. This study provides a guideline to choose appropriate pixel sizes and flight plans to estimate GC and other traits in crop breeding using UAV based HTP platforms.