Estimation of Soil Moisture and Surface Roughness From Single-Polarized Radar Data for Bare Soil Surface and Comparison With Dual- and Quad-Polarization Cases

A new inversion algorithm was developed to determine the surface roughness and soil moisture content of a bare soil surface from a temporal data set of single-polarized radar measurements. This inversion algorithm is simple as it is based on a well-known empirical scattering model, thereby avoiding time-consuming data training. For a temporal data set of single-polarized (VV-, HH-, or VH-polarization) radar measurements, possible combinations of surface root-mean-square (rms) height (h(rms)) and soil moisture contents (Mv) for each measurement were first computed using the scattering model, and all results were then juxtaposed in an h(rms)-Mv plane. Both the surface rms height and the soil moisture content were retrieved using their possibility distributions on the juxtaposed curves. The estimated soil moisture contents and surface rms heights using the juxtaposition/possibility method for single-polarized "time-series" data sets were compared with in situ field measurements and also the retrieval outputs of the inversion algorithms for dual-and quad-polarized "snapshot" data sets. The temporal VV-polarization data sets provided the greatest accuracy among single-polarization cases, with a correlation coefficient of 0.911 and an rmse of 0.034 cm(3)/cm(3).