Crop height is a key structure parameter for the modelling of crop growth, healthy status, yield forecasting and biomass estimation. Unmanned aerial vehicle (UAV) LiDAR systems can quickly and precisely acquire vegetation structure information at a low cost. UAV LiDAR data are increasingly used in vegetation parameters estimation. In this study, we estimated maize and soybean heights using two methods, i.e., based on LiDAR-derived CHM and based on LiDAR variables. The results show that UAV LiDAR data can successfully estimate maize and soybean heights. We found that the method based on LiDAR variables can produce more accurate estimates than CHM-based method. The estimation model of combined maize and soybean had a better prediction performance than those of the specific maize and soybean. Moreover, the soybean height estimation models derived from both methods yielded the lowest prediction precision. We studied the influence of LiDAR point density on crop height estimates through reduced point density (0.25–420 points/m²). When LiDAR point density was less than 1 point/m², the estimation precision for the specific maize and soybean dropped rapidly with the decrease of point density. However, the point density had no significant influence on crop height estimation precision while LiDAR point density was greater than or equal to 1 point/m². Moreover, the original point density did not generate the highest estimation precision in our study. Therefore, high LiDAR point density may be not required for estimating vegetation parameters, and a good balance between the point density and data acquisition cost should be found.

作物高度是作物生长，健康状况，产量预测和生物量估计建模的关键结构参数。无人机（UAV）LiDAR系统可以低成本快速，准确地获取植被结构信息。无人机LiDAR数据越来越多地用于植被参数估计。在这项研究中，我们使用两种方法估算玉米和大豆的高度，即基于LiDAR衍生的CHM和基于LiDAR变量。结果表明，无人机激光雷达数据可以成功地估计玉米和大豆的高度。我们发现，基于LiDAR变量的方法可以比基于CHM的方法产生更准确的估计。玉米和大豆联合的估计模型具有比特定玉米和大豆更好的预测性能。而且，两种方法推导的大豆高度估计模型的预测精度最低。我们通过降低点密度（0.25-420点/ m，研究了LiDAR点密度对作物高度估计的影响²）。当LiDAR点密度小于1点/ m ^2时，特定玉米和大豆的估计精度随点密度的降低而迅速下降。然而，当LiDAR点密度大于或等于1点/ m ^2时，点密度对作物高度估计精度没有显着影响。此外，在我们的研究中，原始点密度没有产生最高的估计精度。因此，可能不需要高LiDAR点密度来估算植被参数，并且应该在点密度和数据获取成本之间找到良好的平衡。