Roughness measurements over an agricultural soil surface with Structure from Motion

摘要

This paper presents an accessible and reliable method to measure surface roughness of agricultural soils with a setup designed to tackle some of the challenges posed by roughness to SAR remote sensing. The method relies on Structure from Motion (SfM). From a large collection of unconstrained images (～700 images) acquired with a commercial-grade camera, digital elevation models (DEMs) are generated for a SAR-pixel-size plot (2×11 m), with horizontal and vertical RMS errors of respectively 1.5 mm and 3.1 mm. Example results highlight the need for individually detrending all sampled sub-DEMs when studying the convergence of the roughness parameters for increasing DEM length. This point appears to be missing in previous publications. The efficiency of the Fourier-based method used to compute the roughness parameters allows investigating anisotropy at a 1° angular resolution. This could benefit investigations on the flashing fields phenomenon observed within narrow direction bands over tilled fields. The inclusion of permanent reference targets into the soil makes multitemporal measurements over the same plot straightforward. Ten acquisitions from April to July 2013 show noticeable natural changes in roughness with cracking during dry periods and smoothing during rainfalls. As expected, changes in RMS height and correlation length appear inversely correlated and can be related to in situ measurements of soil moisture, soil temperature, and rainfall. Analysis of changes in power spectral density indicates that the observed roughness changes only affect scales below 50 cm, i.e. scales relevant for microwave scattering. Even though it seems that millimetric changes for horizontal scales below 1 cm are not observable, measurement performance could be improved by adding more detailed pictures to the image set. This SfM-based method appears to be well-suited to study the dynamics and characterization of roughness for SAR and more generally for geosciences.