Assessment of the influence of UAV-borne LiDAR scan angle and flight altitude on the estimation of wheat structural metrics with different leaf angle distributions

Effective plant area index (ePAI) and vertical ePAI profile are important metrics in the description of vegetation canopy structure. Rapid, accurate, and high-throughput acquisition of crop ePAI and vertical ePAI profiles using uncrewed aerial vehicle-borne LiDAR (UAV-borne LiDAR) is significant in screening high-yielding crop varieties. However, the influence of the flight altitude and scan angle of UAV-borne LiDAR on the acquisition efficiency and estimation accuracy of crop structural phenotypes has not yet been quantified. To fill the gap, this study investigated the influence of the flight altitudes (15 m, 30 m and 45 m) and mean scan angles (9(degrees), 12(degrees), 19(degrees), 20(degrees), 25(degrees), 30(degrees), 35(degrees), 38(degrees) and 52(degrees)) of UAV-borne LiDAR on the estimation of wheat canopy ePAI and vertical ePAI profile for various cultivars and nitrogen (N) fertilization rates. The results showed that the ePAI estimation accuracy decreases with the increase of the mean scan angle. Smaller mean scan angles have higher ePAI estimation accuracy and can capture more accurate vertical ePAI profiles. Higher altitudes not only reduce estimation errors due to smaller mean scan angles, but also reduce flight and data processing time, thus reducing overall costs. The interaction of flight altitude and mean scan angle has an extremely significant effect on the estimation accuracy of ePAI (Adj -R-2 = 0.96, p < 0.001). The results also demonstrate that the accuracy could be greatly improved (RMSE reduced by up to 36.6 %) by considering the leaf angle distribution of different wheat canopies in the ePAI estimation based on Beer-Lambert law.