The Soil Moisture and Ocean Salinity (SMOS) satellite with a passive L-band radiometer monitors surface soil moisture. In addition to soil moisture, vegetation optical thickness is retrieved (L2 product) from brightness temperatures ( , L1C product) using an algorithm based on the L-band Microwave Emission of the Biosphere (L-MEB) model with initial guesses on the two parameters (derived from ECMWF products and ECOCLIMAP Leaf Area Index, respectively) and other auxiliary input. This paper presents the validation work carried out in the Skjern River Catchment, Denmark. L1C/L2 data and the most sensitive algorithm parameters were analyzed by network and airborne campaign data collected within one SMOS pixel (44 km diameter). The SMOS retrieval is based on the prevailing low vegetation class. For the L1C comparison, 's were calculated from in situ soil moisture using L-MEB. Consistent with worldwide findings, the initial/retrieved SMOS soil moisture captures the in situ dynamics well but with significant wet/dry biases and too large amplitudes in case of the latter. While the initial is in range with an in situ estimate for low agricultural vegetation, the retrieved is too high with too pronounced temporal variability. A filter based on L2 criteria removed radio frequency interference (RFI) and improved the between retrieved and network soil moisture from 0.49 to 0.61, while the bias remained . L- kely error sources include the following: 1) still present RFI; 2) potential link between high retrieved and other L-MEB parameters, e.g., low roughness parameter ; 3) 18% lower sand and 8% higher clay fractions while lower bulk density in SMOS algorithm than in situ; and 4) caveats in the Dobson dielectric mixing model implemented in the L-MEB model. A previous study at the Danish validation site had revealed superior performance of the Mironov dielectric mixing model at the 2 2 km scale. Studies are ongoing to address the aforementioned issues, and the role of organic surface layers will be investigated.