Characterization of full surface roughness in agricultural soils using groundbased LiDAR

Microwave emission and scattering models require the parametrization of surface roughness. Traditionally this has been achieved by sampling the surface in transects. In this work, roughness is characterized from 3D surface models derived from ground-based LiDAR. The dataset consist 18 surfaces with varying roughness characteristics. 2D profiles extracted from the surface model constitute the baseline to compare to traditional profiling methods. It was found that sampling using profiles produces an underestimation of the RMSh by 25-63% and an even more severe underestimation in the correlation length that can reach up to an order of magnitude difference. From the 17,178 2D extracted profiles it was determined a significant sensitivity of the roughness parameters to the detrending methods, as well as a poor fit between the experimental ACF and the exponential and Gaussian models. Finally, methodologies to detrend quasi-periodic surfaces and the decomposition of surface at different scales are proposed and illustrate the advantage of having a 3D representation.