On the characterization of agricultural soil roughness for radar remote sensing studies

The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO and IEM) are the root mean square (RMS) height s and autocorrelation length l. However soil moisture retrieval studies based on these models have yielded inconsistent results, not so much because of the failure of the models themselves, but because of the complexity of natural surfaces and the difficulty in estimating appropriate input roughness parameters. In this paper we address the issue of soil roughness characterisation in the case of agricultural fields having different tillage (roughness) states, by making use of an extensive multi-site database of surface profiles collected using a novel laser profiler capable of recording profiles up to 25 metres long. Using this dataset the range of RMS height and correlation values associated with each agricultural roughness state is estimated, and the dependence of these estimates on profile length investigated. The results show that at spatial scales equivalent to those of the SAR resolution cell agricultural surface roughness characteristics are well described by the superposition of a single-scale process related to the tillage state with a multi-scale random fractal process related to field topography.