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, K"s"a"t), 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 (K"s"a"t), 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.
['Michael A. Tischler', 'Matthew Garcia', 'Christa D. Peters-Lidard', 'M. S. Moran', 'Scott N. Miller', 'Daniela Thoma', 'Sujay V. Kumar', 'James Geiger']
A GIS framework for surface-layer soil moisture estimation combining satellite radar measurements and land surface modeling with soil physical property estimation