Winter Wheat LAI Estimation Using UAV Mounted LiDAR

Bates, Jordan Steven [Institute of Bio- and Geosciences, Forschungszentrum of Jülich, Germany] Montzka, Carsten [Institute of Bio- and Geosciences, Forschungszentrum of Jülich, Germany] Schmidt, Marius [Institute of Bio- and Geosciences, Forschungszentrum of Jülich, Germany] Jonard, François [UCL]

Leaf Area Index (LAI) is used to understand and predict crop health and potential yield for farm management. Several remote sensing methods use optical sensors that rely on spectral reflectance to calculate LAI. This can be problematic with cereal crops like winter wheat that lose greenness with decreased chlorophyll and increase in visible browning as they approach senescence stages. Methods with LiDAR have started to emerge using gap fraction to estimate LAI based on canopy density. These methods have been applied to forest cover with Airborne LiDAR Systems (ALS) and have yet to be used with crops such as winter wheat using Unmanned Aerial Vehicle (UAV) LiDAR. This study helps to better understand the potential of LiDAR as a tool to estimate LAI in precision farming.The method proved to have a high to moderate correlation (0.66) in the spatial variation of LAI values with the passive optical method. The LiDAR LAI values were compared to Sunscan SS1 ceptometer ground measurements where it was found to have possibly been overestimated by 18%. There are several flight collection parameters that can be changed to potentially increase the accuracy. This study sheds light on the potential of LiDAR to contribute to LAI metrics in precision farming.