A time-series algorithm is proposed to retrieve surface (from surface down to 1 m depth) soil moisture using the simulated radar data. The time-series approach uses co-polarized (VV and HH) backscattering coefficient (a0) values. Temporal averaging is applied to reduce the radar measurement noise. To the extent that the surface roughness does not change within the time-series window, the reduction of the noise enables the retrieval of the roughness. With the roughness estimate, subsequently soil moisture is retrieved. The proposed retrieval is performed using 'data cubes'. The data cubes relate soil moisture and a0, and are lookup tables with the dimensions of soil moisture, roughness, and vegetation water content (VWC). The cubes are generated by the first-order small perturbation model and the discrete scatterer model for the grass vegetation. A Monte-Carlo analysis demonstrates that the soil moisture may be retrieved within the error better than 0.06cm3/cm3 up to about 3kg/m2 VWC using six time-series records, although presently assuming that the radar model correctly describes the surface scattering processes.
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