Soil moisture retrieval over low-vegetation surfaces using time-series radar observations and a lookup table representation of forward scattering

A radar-based time-series algorithm is evaluated for retrieving soil moisture (from the surface down to 5 cm depth) and roughness using two co-polarized (HH and VV) backscatter cross-section measurements (σ0). The retrieval approach inverts a forward model for radar scattering from a bare surface using a pre-computed lookup-table representation of σ0 obtained from Numerical Maxwell Model in 3D simulations. The retrieval process assumes that surface roughness properties are constant during the time series interval, so that only a single rms height estimate is produced for the entire time series. A study using measured data having 6 to 11 time-steps shows an rms error of 0.044 cm3/cm3 for soil moisture with a correlation coefficient of 0.89 between retrieved and in-situ data. Surface rms height estimates are also found accurate to 10 to 30% of in-situ measurements. It is also shown that retrieval performance is not sensitive to errors in knowledge of the surface roughness correlation length for most of the bare surface conditions examined.