On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation

Data assimilation applications require the development of appropriate mathematical operators to relate model states to satellite observations. Two such "observation'' operators were developed and used to examine the conditions under which satellite microwave and thermal observations provide effective constraints on estimated soil moisture. The first operator uses a two-layer surface energy balance (SEB) model to relate root-zone moisture with top-of-canopy temperature. The second couples SEB and microwave radiative transfer models to yield top-of-atmosphere brightness temperature from surface layer moisture content. Tangent linear models for these operators were developed to examine the sensitivity of modeled observations to variations in soil moisture. Assuming a standard deviation in the observed surface temperature of 0.5K and maximal model sensitivity, the error in the analysis moisture content decreased by 11% for a background error of 0.025 m(3) m(-3) and by 29% for a background error of 0.05 m(3) m(-3). As the observation error approached 2 K, the assimilation of individual surface temperature observations provided virtually no constraint on estimates of soil moisture. Given the range of published errors on brightness temperature, microwave satellite observations were always a strong constraint on soil moisture, except under dense forest and in relatively dry soils. Under contrasting vegetation cover and soil moisture conditions, orthogonal information contained in thermal and microwave observations can be used to improve soil moisture estimation because limited constraint afforded by one data type is compensated by strong constraint from the other data type.