Soil moisture (SM) pre-storm conditions are a key factor in runoff production and can explain much of the observed hydrological response of a basin (e.g., Berthet et al., 2009;Penna et al., 2011). For this reason, the integration of SM observations into hydrological models are considered a valuable practice to improve streamflow predictions (e.g., Coustau et al., 2012;Tramblay et al., 2010).In recent years, SM estimates from remote sensing measurements have experienced growth in their availability and accuracy (Karthikeyan et al., 2017;McCabe et al., 2017). Multiple operational and research satellite SM data sets from active to passive microwave sensors are currently available (e.g., Mecklenburg et al., 2016;Wagner et al., 2013) with coarse-scale surface retrievals and approximately daily coverage. Data assimilation (DA) is a theoretically powerful way to integrate remotely sensed SM observations into hydrological models ; however, its actual benefit is still controversial regarding streamflow prediction, with several studies showing neither systematic nor significant improvements on streamflow prediction, due to a number of technical, mathematical and hydrological factors (e.g.