Abstract
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.