Assessment of the Impact of Uncertainty on Modeled Soil Surface Roughness on Sar-retrieved Soil Moisture Uncertainty

Soil moisture retrieval from SAR images using semi-empirical or physically-based backscatter models requires surface roughness parameters, generally obtained by means of in situ measurements. However, measured roughness parameters often result in inaccurate soil moisture contents. Furthermore, when these retrieved soil moisture contents need to be used in data assimilation schemes, it is important to also assess the retrieval uncertainty. In this paper, a regression-based method is developed that allows for the parameterization of roughness by means of a probability distribution. This distribution is further propagated through an inverse backscatter model in order to obtain probability distributions of soil moisture content. About 70% of the obtained distributions are skewed and non-normal and it is furthermore shown that their interquartile range differs with respect to soil moisture conditions. Comparison of soil moisture measurements with the retrieved median values results in a root mean square error of approximately 3.5 vol%.