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SPECIAL SECTION: FROM FIELD- TO LANDSCAPE-SCALE VADOSE ZONE PROCESSES

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

Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843-2117
^* Corresponding author (bmohanty{at}tamu.edu)

Received 6 March 2005.

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, air- and 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.

Abbreviations: DOY, day of year • EnKF, ensemble Kalman filter • ESTAR, Electronically Scanned Thinned Array Radiometer • GIS, geographic information system • LULC, land use land cover • LW, Little Washita • SGP97, Southern Great Plains 1997 Hydrology experiment • SMAT, soil moisture assessment tool • SVAT, soil–vegetation–atmosphere transfer • TDR, time domain reflectometry

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