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Bias-correction (BC) is typically needed prior to the assimilation of satellite-derived soil moisture (SM) observations in land surface models. A ctive ASCAT and passive SMOS satellit e-derived SM observations are assimilated in the ECMWF integrated forecasting system (IFS). Prior to assimilation, the ASCAT SM observations are bias-corrected using a seasonal rescaling technique. SMOS level 1 observations are converted to level 2 SM via a neural network, which is trained on the global ECMWF operational SM analysis. However, neither of these techniques allow for non-stationary biases and the globally trained SMOS neural network is affected by local biases. In this presentation a two-stage filter is employed in the ECMWF IFS to dynamically correct biases in the SM observations, whilst allowing the assimilation to correct sub-seasonal scale errors. Over a 3-year test period th is adaptive BC approach leads to (i …