|Moran, Mary - Susan|
Submitted to: Journal of Environmental Modeling and Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/5/2006
Publication Date: 2/1/2007
Citation: Tischler, M., Garcia, M., Peters-Lidard, C., Moran, M.S., Miller, S., Thoma, D., Kumar, S., Geiger, J. 2007. A GIS framework for surface-layer soil moisture estimation combining satellite radar measurements and land surface modeling with soil physical property estimation. J. of Environmental Modeling and Software 22:891-898. Interpretive Summary: In a cooperative project with ARS and the U.S. Army Corps of Engineers, satellite image data and simulation modeling were combined for the estimation of surface soil moisture at the watershed scale. The Army wanted a product that was user friendly and allowed visualization by users in the field. A Geographic Information System (GIS) provided a good framework to integrate the models and data on a Windows platform. As a result, a graphical user interface exists for a user to easily parameterize the land surface model, perform data format conversions. This system was designed and developed with the military user in mind, for use in trafficability assessment, construction engineering, and countermine efforts. It allows a user with little formal background in hydrological sciences, remote sensing, computer science, or soil science to determine spatial surface soil moisture using a scientifically sound procedure with robust modeling tools for use in a variety of applications.
Technical Abstract: A GIS framework, the Army Remote Moisture System (ARMS), has been developed to link the Land Information System (LIS), a high performance land surface modeling and data assimilation system, with remotely sensed measurements of soil moisture to provide a high resolution estimation of soil moisture in the near surface. ARMS uses available soil (soil texture, porosity, Ksat), land cover (vegetation type, LAI, Fraction of Greenness), and atmospheric data (Albedo) in standardized vector and raster GIS data formats at multiple scales, in addition to climatological forcing data and precipitation. PEST (Parameter EStimation Tool) was integrated into the process to optimize soil porosity and saturated hydraulic conductivity (Ksat), using the remotely sensed measurements, in order to provide a more accurate estimate of the soil moisture. The modeling process is controlled by the user through a graphical interface developed as part of the ArcMap component of ESRI ArcGIS.