OBJECTIVE: Define and develop scaling algorithms to support rapid computation and identification of Army scale soil strength DESCRIPTION: The computation of high resolution near surface profiles of soil moisture is critical to determining soil strength for Army scale mobility predictions, DoD target acquisition and identification, and flood mapping. There is a significant technology gap between the ability to compute high resolution soil moisture maps in austere environments due resolution limits in remote sensing and weather modeling technologies (weather scale). New methods are needed to bridge the gap between the limits of available coupled weather and land surface modeling science with target and mobility scale resolutions required by Army. This topic addresses a specific goal of developing mathematical modeling method designed to bridge the gap between weather scale and Army scale. PHASE I: This proposed SBIR seeks to define downscaling and up scaling algorithms and related available software and hardware to support rapid identification of areas of low surface soil strength and related soil moisture. The study is expected to support mobility predictions, buried explosive detection, improved flood models, and other related terrestrial engineering studies. The downscaling algorithms will support current data sources for soil moisture at the global scale as generated at resolutions of 25 kilometers or less as distributed by the Land Information System (LIS) generated by the Air Force Weather Agency (AFWA). Currently the LIS fails to define the localized extremes in soil moisture variability occurring due to elevation changes, vegetation, soil type, or other related factors. Local weather stations provide only point data requiring upscaling of information to define soil moisture outside the local area. The investigator will address innovative techniques to support in downscaling and up scaling soil moisture data. The required deliverables in Phase I will include bi weekly progress reports, a prototype algorithm supporting input of low resolution soil moisture along with correlated attributes (as defined in this study) with outputs of high-resolution moisture data, and supporting ground truth documents of the down scaling and up scaling programs. The small business will produce a conceptual design and breadboard supporting the development and demonstration of a prototype program to downscale moisture data derived from existing LIS AFWA supplied data sets, elevation data, soils data, and any other data sources determined as highly correlated to changes in soil moisture. Obtaining soil moisture grids at 1 to 25 kilometer resolution, the small business will define soil moisture variations at scales of the existing elevation grids of 100 meters or less. The resulting algorithms should be sufficiently portable enough to allow integration into a Geographic Information System (GIS) or Open Geospatial Consortium (OGC) compliant mapping system. Simple interpolation algorithms are limited due to the complex nature of multiple hydrologic processes affecting the soil moisture. Conversion of this soil moisture to standard units such as rating cone index, to support maneuver predictions would be part of the research effort. The research will provide algorithms which produce high resolution soil strength and/or soil moisture maps at minimum resolutions of 100 meters. The product would identify calibration sources and define the expected error in the predictions for risk assessment. This phase will demonstrate the feasibility of producing a demonstration of downscaling of soil moisture data within the US and Afghanistan, and will outline the demonstration success criteria by the generation of a first generation downscaling/up scaling tool box. The study will include reviews of innovative methods to validate the new high resolution maps of soil moisture. PHASE II: The contractor will develop, demonstrate, and validate the down scaling and up scaling algorithms created in phase I with sites in the US and at least one site overseas. Required phase II deliverables will include source code and program which takes as input low resolution global soil moisture data and related information from AirForce Weather Agency and process this data using correlated terrain data such as elevation to resolutions of 100 meters or less. The soil moisture data should be exhibited in terms of volumetric and gravimetric data using the most current soils information for the region. The data for the top 10 centimeters should show good correlations with ground truth collected in the regions. The deliverables will include sample data supporting the downscaling technique for a region no less than 40 kilometers by 40 kilometers in the US and a least one site overseas. The program is expected to be a toolbox within ArcGIS which takes as input the data source required to generate the high resolution data grid. The contractor will also develop, demonstrate, and validate a up scaling algorithm as a component of a GIS or OGC-compliant web mapping service which takes as input a one or more weather stations and generates the a grid of soil moisture at the resolution of 100 meters or less within a region of 40 km by 40 km. The contractor shall demonstrate the accuracy of both the downscaling and upscaling techniques using a jackknife statistical approach. The correlation between the predictions will be supplied as an additional attribute in the shape file created from the ArcGIS toolbox. During Phase II the contractor will have biweekly telecoms with the government POC and provide a report each quarter detailing progress. Updated versions of the program will be provided to the government at the end of the study along with source code and documentation. PHASE III: Numerous industries within the U.S. see impacts from high soil moisture or weakened soils, including logging, construction, and agriculture. The ability to compute high resolution soil strength would benefit each commercial sector. Inversely, the ability to compute high resolution soil moisture states for agriculture, better identifying areas of excessively moist or dry soils at small field-scale resolutions, could lead to better improved irrigation and fertilization efficiencies.