Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown in Wageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m 2 . The 3DPI algorithm proved to estimate biomass well for sugar beet (R 2 = 0.68, RMSE = 17.47 g/m 2 ) and winter wheat (R 2 = 0.82, RMSE = 13.94 g/m 2 ). Also, the height estimates worked well for sugar beet (R 2 = 0.70, RMSE = 7.4 cm) and wheat (R 2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R 2 = 0.50, RMSE = 12 cm) and biomass estimation (R 2 = 0.24, RMSE = 22.09 g/m 2 ), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly. good correlation with in situ field measurements of plant height. Sun et al. [4] published an R 2 of 0.98 for cotton plants, [2] published an R 2 of 0.99 for wheat, and [7] showed an R 2 of 0.90 for wheat. These studies show the capability of LiDAR to measure basic phenotypes such as plant height. However, LiDAR systems mounted on tractors can be unsuitable for labour-intensive crops such as rice or in orchards [8], for example, due to compaction of the soil [9]. A UAV equipped with a LiDAR system can overcome those limitations.Earlier research on the relation between plant height and biomass was based on varying approaches for plant height measurements. Madec et al. [7] found an R 2 of 0.88 for the correlation between plant height and field-measured biomass, using a tractor based LiDAR system. Bendig et al.[10] used a structure from a motion (SfM) technique on UAV acquired imagery to derive plant height and found an R 2 of 0.81 between field-measured height and SfM derived height.In the last few years, LiDAR systems have been miniaturized, resulting in lower weights and reduced dimensions, and as a result, can be operated from UAVs. This development opens the way towards high throughput derived, more complex products like biomass and yield, thus, improving the speed and frequency at which these plant traits can be acquired in the field in an undisturbed way.LiDAR-based biomass estimates of agricultural crops can be derived in different ways. Based on the Lambert-Beer LAI model of [11], the authors of [2] developed a biomass prediction model called the 3-Dimensional Profile Index (3DPI). Where the LIDAR 3D point cloud is divided into layers...

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