Assimilating remote sensing data in a surface flux-soil moisture model
William L. Crosson, Charles A. Laymon, Ramarao Inguva and Marius P. Schamschula
A key state variable in land surface–atmosphere interactions is soil moisture, which affects surface energy fluxes,
runoff and the radiation balance. Soil moisture modelling relies on parameter estimates that are inadequately measured
at the necessarily fine model scales. Hence, model soil moisture estimates are imperfect and often drift away from
reality through simulation time. Because of its spatial and temporal nature, remote sensing holds great promise for
soil moisture estimation. Much success has been attained in recent years in soil moisture estimation using passive
and active microwave sensors, but progress has been slow. One reason for this is the scale disparity between remote
sensing data resolution and the hydrologic process scale. Other impediments include vegetation cover and microwave
penetration depth. As a result, currently there is no comprehensive method for assimilating remote soil moisture
observations within a surface hydrology model at watershed or larger scales.
This paper describes a measurement–modelling system for estimating the three-dimensional soil moisture distribution,
incorporating remote microwave observations, a surface flux–soil moisture model, a radiative transfer model
and Kalman filtering. The surface model, driven by meteorological observations, estimates the vertical and lateral distribution
of water. Based on the model soil moisture profiles, microwave brightness temperatures are estimated using
the radiative transfer model. A Kalman filter is then applied using modelled and observed brightness temperatures to
update the model soil moisture profile.
The modelling system has been applied using data from the Southern Great Plains 1997 field experiment. In the
presence of highly inaccurate rainfall input, assimilation of remote microwave data results in better agreement with
observed soil moisture. Without assimilation, it was seen that the model near-surface soil moisture reached a minimum
that was higher than observed, resulting in substantial errors during very dry conditions. Updating moisture profiles
daily with remotely sensed brightness temperatures reduced but did not eliminate this bias.