Radar based surface soil moisture retrieval through the combined use of two backscattering models

Radar based surface soil moisture retrieval has been subject of intense research during the last decades. However, several difficulties hamper the operational estimation of soil moisture based on actually available space borne sensors. The main difficulty experienced so far consists of the parameterization of other surface characteristics, mainly roughness, which strongly influences the backscattering coefficient and harms the soil moisture inversion. This fact, along with the high spatial variability of the surface roughness parameters, makes it necessary to perform intensive roughness measurements in order to invert soil moisture values with an adequate accuracy, what reduces the applicability of the approach. This paper reviews an approach, proposed by Pauwels et al. 8, in which a combined application of two well documented backscattering models, i.e. the Integral Equation Method model and the Oh model, is carried out following an iterative scheme. The approach can be applied to single configuration scenes acquired over homogeneous roughness conditions and yields estimates of both soil moisture and roughness parameters without performing ground measurements of soil moisture or roughness. The proposed algorithm was applied to a set of five RADARSAT-1 scenes acquired over Navarre (Spain) between February and April 2003. Inverted soil moisture and surface roughness parameters were compared to ground measured reference values over an experimental watershed. Results are encouraging and the possibility of simultaneously estimating both variables opens new application scenarios for radar remote sensing on the study of numerous processes at the soil surface.

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