Root Zone Soil Moisture Assessment Using Remote Sensing and Vadose Zone Modeling

Soil moisture is an important hydrologic state variable critical to successful hydroclimatic and environmental predictions. Soil moisture varies both in space and time because of spatio-temporal variations in precipitation, soil properties, topographic features, and vegetation characteristics. In recent years, airand space-borne remote sensing campaigns have successfully demonstrated the use of passive microwave remote sensing to map soil moisture status near the soil surface (»0–0.05 m below the ground) at various spatial scales. In this study root zone (e.g., »0–0.6 m below the ground) soil moisture distributions were estimated across the Little Washita watershed (Oklahoma) by assimilating near-surface soil moisture data from remote sensing measurements using the Electronically Scanned Thinned Array Radiometer (ESTAR) with an ensemble Kalman filter (EnKF) technique coupled with a numerical one-dimensional vadose zone flow model (HYDRUS-ET). The resulting distributed root zone soil moisture assessment tool (SMAT) is based on the concept of having parallel noninteracting streamtubes (hydrologic units) within a geographic information system (GIS) platform. The simulated soil moisture distribution at various depths and locations within the watershed were compared with measured profile soil moisture data using time domain reflectometry (TDR). A reasonable agreement was found under favorable conditions between footprint-scale model estimations and point-scale field soil moisture measurements in the root zone. However, uncertainties introduced by precipitation and soil hydraulic properties caused suboptimal performance of the integrated model. The SMAT holds great promise and offers flexibility to incorporate various data assimilation techniques, scaling, and other hydrological complexities across large landscapes. The integrated model can be useful for simulating profile soil moisture estimation and for predicting transient soil moisture behavior for a range of hydrological and environmental applications. S DISTRIBUTIONS of soil moisture status in the root zone across large land areas provide important input for many agricultural, hydrological, and meteorological applications (Hanson et al., 1999). Also, estimation of root zone soil moisture at various temporal and spatial scales is key to strategic management of water resources. Root zone soil moisture is a critical storage parameter, which controls partitioning of energy and mass related to evapotranspiration and runoff (Georgakakos, 1996). Precipitation, soil texture, topography, land use, and a variety of meteorological variables influence the spatial distribution and temporal evolution of root zone soil moisture. Many studies at the Southern Great Plains 1997 Hydrology Experiment (SGP97) site have examined how these variables influence the spatiotemporal distribution of soil moisture and surface fluxes (Famiglietti et al., 1999; Mohr et al., 2000; Mohanty et al., 2000a, 2000b; Mohanty and Skaggs, 2001; Bindlish et al., 2001; Kustas et al., 2001; Wickel et al., 2001). The estimation of soil moisture and energy– mass exchange is simulated using soil–vegetation– atmosphere transfer models (SVAT). The accuracy of SVAT models is usually restricted by unreliable estimates of root zone soil moisture (Koster and Milly, 1997). Despite the significance of root zone soil moisture in hydrological and meteorological predictions, detailed spatiotemporal modeling of root zone soil moisture at the regional or global scale is often lacking. Root zone soil moisture distributions are best assessed by periodic gravimetric sampling or by calibrated TDR techniques. At a particular location, soil moisture can be continuously monitored by calibrating segmented TDR probes (e.g., Hook and Livingston, 1996) or by multisensor capacitance probes (e.g., Starr and Paltineanu, 1998). Camillo and Schmugge (1983) retrieved root zone soil moisture estimates from surface measurement for dry soil with fully grown roots using a linear relationship between moisture content in the two soil layers based on a simple solution of Richards’ equation. These techniques serve well for field plot or localscale monitoring but are not feasible for the watershed or regional scale. Remote sensing of soil moisture from airor space-borne platforms has the ability to overcome this problem and provide large spatial coverage and temporal continuity. In the last three decades studies have successfully established the use of passive microwave remote sensing to measure the surface wetness (Engman and Gurney, 1991; Jackson, 1993; Njoku and Entekhabi, 1995; Jackson et al., 1999). These measurements described soil moisture in a thin soil layer, usually up to a depth of 0.05 m below the soil surface (Schmugge et al., 1974, 1977, 1980; Jackson and Schmugge, 1989). However, an associated problem that has hindered the measurement of soil moisture from air and space using passive microwave techniques is its coarse spatial and temporal resolution, which is not consistent with the scale of hydrologic processes of interest. Prevot et al. (1984) demonstrated that the soil water balance could be determined with equal accuracy using remotely sensed surface soil moisture estimates substituted for in situ observations. Smith and Newton (1983) developed a soil water simulation model that used remotely sensed data to predict profile soil moisture. In N.N. Das and B.P. Mohanty, Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843-2117.Received 6Mar. 2005. *Corresponding author (bmohanty@