 The study aims to develop a method for deriving crop height and growth rate rapidly from multi-temporal, very high spatial resolution, 1 centimeter per pixel, 3D digital surface models of crop field trials produced via structure for motion, SFM, photogrammetry using aerial imagery collected to repeated campaigns flying an unmanned aerial vehicle, UAV, with amounted red-green blue, RGB, camera. The study compares UAV-SFM modeled crop heights to those derived from terrestrial laser scanner, TLS, and to the standard field measurement of crop height conducted using a 2M rule. The results show that the optimized UAV method was able to achieve a root mean-squared error, RMSE, of 0.07, 0.02 and 0.03M for May, June and July, respectively, enabling crop growth rate to be derived from differencing of the multi-temporal surface models. The study also found that digital surface models produced provide a novel spatial mapping of crop height variation, both at the field scale and also within individual plots. Overall, the study demonstrates the potential of UAV-based SFM as a new standard for high throughput phenotyping of infield crop heights. This article was authored by Fenner H. Holman, Andrew B. Riesch, Adam Michalski, and others. We are article.tv, links in the description below.