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Burgin, M., and van Zyl, J. (10-July-16)

State of the art soil moisture radar retrieval algorithms traditionally depend on substantial amounts of ancillary data, such as land cover and soil texture/composition maps, to parametrize complex electromagnetic models. In this work, we pursue an existing empirical approach as an alternative; it expresses radar backscatter of a vegetated scene as a linear function of soil moisture, thus reducing the dependence on ancillary data. We use 2.5 years of L-band Aquarius radar and radiometer derived soil moisture data to determine the two unknowns of the linear model function on a global scale. We investigate the impact of land cover type by utilizing the widely used IGBP land cover classification; it is found to be significant. We observe seasonal variation in the radar sensitivity to soil moisture, indicating and quantifying seasonally changing vegetation. Finally, we investigate the impact of vegetation heterogeneity within a radar pixel.