Remote sensing for the estimation of surface soil moisture (SSM) is performed mainly with active and passive sensors working in the microwave region of the electromagnetic spectrum. Airborne microwave measurements (Hajnsek et al. 2009; Saleh et al. 2009) are often recorded for a short time period only. This makes it difficult to consider these data in numerical models in an operational way. In contrast, spaceborne sensors like ASCAT (Bartalis et al. 2007), AMSR-E (Njoku et al. 2003), SMOS (Kerr et al. 2010), or PALSAR (Takada et al. 2009) provide information on SSM from the regional to the global scale (Wagner et al. 2007) with long-term repetitive coverage. This is important for further utilization of SSM records in numerical modeling (Munoz-Sabater et al. 2012; Scipal et al. 2008). Because soil moisture was recognized as an essential climate variable (GCOS 2010), long-term time series starting in 1979 have been created for global-scale applications by merging SSM products from different active and passive microwave satellite sensors into a single dataset (Dorigo et al. 2012).