Feasibility of Estimating Soil Surface Roughness and Vegetation Opacity from Disaggregated SMOS Brightness Temperatures
Mboh, C. M.; Montzka, C.; Kumbhar, P.; Vereecken, H.
Forschungszentrum Juelich, GERMANY

Good knowledge of the spatio-temporal variations of soil moisture at the catchment and regional scales is critical to understanding the terrestrial water cycle (Jung et al., 2010) and its impact on the evolution of the weather, climate change (Senevirante et al., 2010) and the management of soil and water resources. Through MIRAS (Microwave Imaging Radiometer by Aperture Synthesis) the radiometer on board the SMOS (Soil Moisture and Ocean Salinity) satellite, the European Space Agency (ESA) has been providing two-dimensional brightness temperature images of the Earth surface at a frequency of 1.4 GHz (L-band) since November 2009.These brightness temperature images are provided at an average spatial resolution of about 40 Km, a temporal resolution of 1-3 days and a radiometric sensitivity that ranges between 0.8 and 3K. Due to the large contrast between the dielectric properties of liquid water and dry soil at L-band, SMOS brightness temperatures are sensitive to soil moisture. Radiative transfer models Like L-MEB (L-band Microwave Emission of the Biosphere; Wigneron et al., 2007) and CMEM (Community Microwave Emission Modeling platform, Holmes et al., 2008) have been developed to retrieve soil moisture from brightness temperature. The expected accuracy of the soil moisture maps so obtained is about 0.04cm3cm3 (Kerr et al., 2010). Besides soil moisture, brightness temperatures are also influenced by other surface characteristics like soil texture, soil roughness, surface temperature and vegetation opacity. Soil surface roughness and vegetation opacity has been observed to greatly influence brightness temperatures and the corresponding soil moisture retrievals (Wigneron et al., 2007; Grant et al., 2008; Saleh et al., 2009). For instance Wigneron et al. (2007) noted that, an absolute error of 0.5 on the soil surface roughness above a corn field can lead to errors in the order of 0.07 m3/m3 on the retrieved moisture content. At the local scale soil surface roughness and vegetation opacity are expected to vary spatially and temporally with phenological stages. Although at scales of 1 Km and above, effective values for theses radiative parameters might be relatively constant from one geographic location to another because of the mixing of a variety of local land cover conditions, the impact of their temporal variability on soil moisture retrieval cannot be underestimated. From a numerical stand point, this study will investigate the feability of estimating the soil surface roughness and the vegetation opacity in the Rur and Erft catchments in Germany based on simulated brightness temperatures at 1Km resolution using CMEM and the SCE-AU (Shuffled Complex Evolution Optimization Algorithm, Duan et al., 1993). There has been rapid progress on the disaggregation or downscaling of SMOS products (e.g Merlin et al., 2008; Piles et al., 2011; Merlin et al., 2013). This study will therefore provide a basis for assessing the information content of SMOS brightness temperatures downscaled to a few kilometers. Data assimilation (DA) approaches have been used in the past to address a similar problem ( e.g Montzka et al., 2012). Similar to DA approaches, our approach will also account for the propagation of uncertainty from some of the meteorological forcing on the simulated brightness temperatures and provide uncertainty bounds on the estimated radiative transfer parameters.


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