Abstract
A time-series algorithm is proposed to retrieve surface (from surface down to 1m depth) soil moisture using the simulated radar data. The time-series approach uses co-polarized (V V and HH) backscattering coe-cient (ae 0 ) 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 per- formed using 'data cubes'. The data cubes relate soil moisture and ae 0 , and are lookup tables with the dimensions of soil moisture, roughness, and vegetation water content (VWC). The cubes are generated by the flrst-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 0.06cm 3 /cm 3 up to about 3kg/m 2 VWC using six time- series records, although presently assuming that the radar model correctly describes the surface scattering processes.
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