Abstract. Surface soil moisture content is highly variable in both space and time. While remote sensing provides an effective methodology for mapping surface moisture content over large areas, it averages within-pixel variability thereby masking the underlying heterogeneity observed at the land surface. This variability must be better understood in order to rigorously evaluate sensor performance and to enhance the utility of the larger-scale remotely sensed averages by quantifying the underlying variability that remote sensing cannot record explicitly. In support of the Southern Great Plains 1997 (SGP97) Hydrology Experiment (a surface soil moisture mapping mission conducted between June 18 and July 17, 1997, in central Oklahoma) an investigation was conducted to characterize soil moisture variability within remote sensing footprints (approximately 0.64 km 2 ) with more certainty than would be afforded with conventional gravimetric moisture content sampling. Nearly every day during the experiment period, portable impedance probes were used to intensively monitor volumetric moisture content in the 0-to 6-cm surface soil layer at six footprint-sized fields scattered over the SGP97 study area. A minimum of 49 daily moisture content measurements were made on most fields. Higher-resolution grid and transect data were also collected periodically. In total, more than 11,000 impedance probe measurements of volumetric moisture content were made at the six sites by over 35 SGP97 participants. The wide spatial distribution of the sites, combined with the intensive, near-daily monitoring, provided a unique opportunity (relative to previous smaller-scale and shorter-duration soil moisture studies) to characterize variations in surface moisture content over a range of wetness conditions. In this paper the range and temporal dynamics of the variability in moisture content within each of the six fields are described, as are general relationships between the variability and footprint-mean moisture content. Results indicate that distinct differences in mean moisture content between the six sites are consistent with variations in soil type, vegetation cover, and rainfall gradients. Within fields the standard deviation, coefficient of variation, skewness, and kurtosis increased with decreasing moisture content; the distribution of surface moisture content evolved from negatively skewed/nonnormal under very wet conditions, to normal in the midrange of mean moisture content, to positively skewed/nonnormal under dry conditions; and agricultural practices of row tilling and terracing were shown to exert a major control on observed moisture content variations. Results presented here can be utilized to better evaluate sensor performance, to extrapolate estimates of subgrid-scale variations in moisture content across the entire SGP97 region, and in the parameterization of soil moisture dynamics in hydrological and land surface models.

No abstract

Abstract: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.

No abstract