Submitted to: Remote Sensing of Environment
Publication Type: Peer reviewed journal
Publication Acceptance Date: 12/4/2006
Publication Date: 4/15/2008
Citation: Crow, W.T., Kustas, W.P., 2008. Monitoring root-zone soil moisture through the assimilation of a thermal remote sensing-based soil moisture proxy into a water balance model. Remote Sensing of Environment. 112(4):1268-1281. Interpretive Summary: This research looks at ways to combine two distinct methodologies for tracking the availability of root-zone soil moisture in agricultural landscapes. One method is based on observing rainfall and dynamically updating a root-zone water balance model. The other is based on thermal remote sensing observations which are used to detect an increase in vegetation canopy temperatures associated with water stress. A mathematical strategy for integrating the two methodologies is developed and the approach is applied to long-term data set collected at Beltsville. Results suggests that combining the two approaches can lead to improved accuracy for root-zone soil moisture predictions. Such predictions are useful for water quality modeling, irrigation scheduling and crop yield forecasting applications.
Technical Abstract: Two types of Soil Vegetation Atmosphere Transfer (SVAT) modeling approaches can be applied to monitor root-zone soil moisture in agricultural landscapes. Water and Energy Balance (WEB) SVAT modeling is based on forcing a prognostic root-zone water balance model with observed rainfall and predicted evapotranspiration. In contrast, thermal Remote Sensing (RS) observations of surface radiometric temperature (TR) are integrated into purely diagnostic RS-SVAT models to predict the onset of vegetation water stress. While RS-SVAT models do not explicitly monitor soil moisture, they can be used in the calculation of thermal-based proxy variables for the availability of soil water in the root zone. Using four growing seasons (2001 to 2004) of profile soil moisture, micro-meteorology, and surface radiometric temperature measurements at the United States Department of Agriculture (USDA) Optimizing Production Inputs for Economic and Environmental Enhancements (OPE3) study site in Beltsville, MD, prospects for improving WEB-SVAT root-zone water predictions via the assimilation of diagnostic RS-SVAT soil moisture proxy information are examined. Results illustrate the potential advantages of such an assimilation approach relative to the competing approach of directly assimilating TR measurements. Since TR measurements used in the analysis are tower-based (and not obtained from a remote platform), a sensitivity analysis demonstrates the potential impact of remote sensing limitations on the value of the RS-SVAT proxy. Overall, results support a potential role for RS-SVAT modeling strategies in improving WEB-SVAT model characterization of root-zone soil moisture.