Crop Biophysical Properties Estimation Based on LiDAR Full-Waveform Inversion Using the DART RTM

This paper presents the results of a three-dimensional (3-D) model inversion in order to demonstrate the potential of small footprint light detection and ranging (LiDAR) waveforms for estimating crop biophysical properties. For such, we consider the height, leaf area index (LAI), and ground spectral reflectance of two maize and wheat fields. Crop structure spatial variability that is observed per measured waveform is a source of inaccuracy for the inversion of LiDAR small footprint waveforms. For example, in the maize field, standard deviation is 0.16 m for height and 0.6 for LAI. To mitigate this issue, all measured waveforms are first classified into maize and wheat clusters. Then, biophysical properties are assessed per cluster using a look-up table of waveforms that are simulated by the discrete anisotropic radiative transfer model that works with the LiDAR configuration and realistic crop 3-D mock-ups with varied properties. Results were tested against in situ measurements. Crop height is very well estimated, with root-mean-square error (RMSE) approximate to 0.07 and 0.04 m for maize and wheat, respectively. LAI estimate is also accurate (RMSE = 0.17) for maize except for wheat last growth stage (RMSE = 0.5), possibly due to the wheat low LAI value. Finally, the field spatial heterogeneity justifies the selection of many clusters to get accurate results.