Validation of SMOS L1C and L2 Products and Important Parameters of the Retrieval Algorithm in the Skjern River Catchment, Western Denmark

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 <formula formulatype="inline"><tex Notation="TeX">$\tau_{\rm NAD}$</tex></formula> is retrieved (L2 product) from brightness temperatures (<formula formulatype="inline"><tex Notation="TeX"> $T_{B}$</tex></formula>, 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, <formula formulatype="inline"><tex Notation="TeX">$T_{B}$</tex></formula>'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 <formula formulatype="inline"><tex Notation="TeX">$\tau_{\rm NAD}$</tex></formula> is in range with an in situ estimate for low agricultural vegetation, the retrieved <formula formulatype="inline"><tex Notation="TeX">$\tau_{\rm NAD}$</tex></formula> is too high with too pronounced temporal variability. A filter based on L2 criteria removed radio frequency interference (RFI) and improved the <formula formulatype="inline"><tex Notation="TeX">$R^{2}$</tex></formula> between retrieved and network soil moisture from 0.49 to 0.61, while the bias remained <formula formulatype="inline"><tex Notation="TeX">$(-0.092/-\! 0.087\ \hbox{m}^{3}/\hbox{m}^{3})$</tex> </formula>. Likely error sources include the following: 1) still present RFI; 2) potential link between high retrieved <formula formulatype="inline"><tex Notation="TeX">$\tau_{\rm NAD}$</tex></formula> and other L-MEB parameters, e.g., low roughness parameter <formula formulatype="inline"><tex Notation="TeX">$(H_{R})$</tex></formula>; 3) <formula formulatype="inline"> <tex Notation="TeX">$\sim$</tex></formula>18% lower sand and <formula formulatype="inline"><tex Notation="TeX">$\sim$</tex></formula>8% higher clay fractions while <formula formulatype="inline"><tex Notation="TeX">$\sim\! \! 0.35\ \hbox{g/cm}^{3}$</tex></formula> 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 <formula formulatype="inline"><tex Notation="TeX">$\times$ </tex></formula> 2 km scale. Studies are ongoing to address the aforementioned issues, and the role of organic surface layers will be investigated.