Assessment of SAR-retrieved soil moisture uncertainty induced by uncertainty on modeled soil surface roughness

The Integral Equation Model (IEM) is frequently used to retrieve moisture content of bare soils from synthetic aperture radar (SAR) images. This physically-based backscatter model requires surface roughness parameters, generally obtained by in situ measurements, which unfortunately often result in inaccurately retrieved soil moisture contents. Furthermore, when the retrieved soil moisture contents need to be used in data assimilation applications, it is important to also assess the retrieval uncertainty. Therefore, in this paper a regression-based method is developed that allows for the parameterization of roughness and that provides an estimation of its uncertainty by means of a probability distribution. By further propagating this distribution through the inversion of the IEM, a probability distribution of soil moisture content is obtained. It was found that 70% of the thus obtained distributions are skewed and non-normal. Furthermore, it is shown that their interquartile range varies depending on soil moisture conditions. Comparison of soil moisture measurements with the retrieved median values of the soil moisture histograms results in a root mean square error (RMSE) of approximately 3.5 vol%.

► We employ a regression-based method that allows for the parameterization of roughness and that provides an estimation of its uncertainty. ► Propagating this uncertainty through the inverse IEM yields a probability distribution of soil moisture content. ► 70% of the obtained distributions are skewed and non-normal. ► The interquartile range varies depending on the soil moisture conditions.