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 roof: mean square (RMS) height s, and auto-correlation length l, However, soil moisture retr ieval 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 appropriat e input roughness parameters. In this paper, we address the issue of soil r oughness characterization in the case of agricultural fields having differe nt tillage (roughness) states by making use of an extensive multisite datab ase of surface profiles collected using a novel laser profiler capable of r ecording profiles up to 25 m long. Using this dataset, the range of RMS hei ght and correlation values associated with each agricultural roughness stat e is estimated, and the dependence of these estimates on profile length is investigated. The results show that at spatial scales equivalent to those o f the SAR resolution cell, agricultural surface roughness characteristics a re well described by the superposition of a single scale process related to the tillage state with a multiscale random fractal process related to fiel d topography.