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

Abstract

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 rootzone 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 0.5 K and maximal model sensitivity, the error in the analysis moisture content decreased by 11% for a background error of 0.025 m 3 m 23 and by 29% for a background error of 0.05 m 3 m 23 . 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.

Water resource monitoring systems and the role of satellite observations

“…The information content of observations also varies as a function of transient vegetation and soil moisture conditions. Experiments with a precursor of AWRA-L indicated that microwave and TIR observations only impart useful information under certain conditions: microwave emissions are informative for topsoil wetness in sparsely vegetated areas, whereas TIR can constrain root-zone water content over vegetated areas (Barrett and Renzullo, 2009).…”

Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China

“…Importantly, surface soil moisture could be used to obtain better characterization of the root zone soil moisture (Barrett and Renzullo, 2009;Crow et al, 2008;Das et al, 2008;Draper et al, 2011;Li et al, 2010). It has also shown that the assimilation of soil moisture observations can be used to correct rainfall errors (Crow et al, 2011;Yang et al, 2009).…”

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