Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry

AbstractMethods of estimating the total amount of above-ground biomass (AGB) in crop fields are generally based on labourious, random, and destructive in situ sampling. This study proposes a methodology for estimating herbaceous crop biomass using conventional optical cameras and structure from motion (SfM) photogrammetry. The proposed method is based on the determination of volumes according to the difference between a digital terrain model (DTM) and digital surface model (DSM) of vegetative cover. A density factor was calibrated based on a subset of destructive random samples to relate the volume and biomass and efficiently quantify the total AGB. In all cases, RMSE Z values less than 0.23 m were obtained for the DTM-DSM coupling. Biomass field data confirmed the goodness of fit of the yield-biomass estimation (R2=0.88 and 1.12 kg/ha) mainly in plots with uniform vegetation coverage. Furthermore, the method was demonstrated to be scalable to multiple platform types and sensors.