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Title:   Author  item
                                            Bolten, Johnitem
                                            Crow, Wadeitem
                                            ZHAN, X - NOAA NESDIS STARitem
                                            Reynolds, Curtitem
                                            Jackson, Thomas
  Submitted to: Springer Verlag
                                    Publication Type: Book / Chapter
                                    Publication Acceptance Date: 5/1/2008
                                    Publication  Date: 3/9/2009
                                    Citation: Bolten, J.D., Crow, W.T., Zhan, X., Reynolds, C.A., Jackson, T.J. 2009. Assimilation of a satellite-based soil moisture product into a two-layer water balance model for a global crop production decision support system. In: Pard, S.K., editor. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. London, United Kingdom: Springer-Verlang. p. 449-464.Interpretive Summary: A chief function of the USDA Foreign Agricultural Service (FAS) is the world-wide monitoring of crop conditions and prediction of crop yield and acreage under tillage for various commodity crops.  These forecasts are used both for national security purposes as well as to improve the global competitiveness of U.S. agricultural exports.  A key part of such monitoring is the detection of agricultural drought and its impact on crop yield.  If properly considered, remotely sensed surface soil moisture retrievals from satellites can play a key role in mapping the global extent of drought conditions.  This research describes the development of a monitoring system (based on newly developed data assimilation techniques) designed to optimally merge remotely-sensed soil moisture retrievals into the existing USDA FAS drought monitoring system.  Follow-up work will quantify the degree to which the data assimilation system is capable of improving drought and crop yield forecasts.
                                Technical Abstract: The monitoring of global food supplies performed by the U. S. Department of Agriculture (USDA) Production Estimates and Crop Assessment Division (PECAD) is essential for early warning of food shortages, and providing greater economic security within the agriculture sector. Monthly crop yield and forecasting is calculated by PECAD through a combination of climatic and land surface data integrated into land surface models within the Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System (DSS). The accuracy of this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal coverage of the land and climatic data input into the models.

Soil moisture is a fundamental data source used in the crop growth stage and crop stress models. Currently, the PECAD DSS utilizes a modification of the Palmer two-layer soil moisture model to estimate surface soil moisture. Inputs into this model include soil parameter values of soil water holding capacity, daily precipitation and temperature estimates provided by weather data from the Air Force Weather Agency (AFWA) and precipitation observations from the world Meteorological Organization (WMO). These sources provide secondary estimates of soil moisture and may be improved by the addition of direct observations of soil moisture.

Using recently developed data assimilation techniques, this study aims at improving the soil moisture estimates used by the PECAD by integrating soil moisture observations from the NASA EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA DSS.  Launched in 2002, the AMSR-E instrument is capable of providing a full global coverage soil moisture product over lightly vegetated areas every 2-3 days. The improved spatial and temporal resolution of AMSR-E upon the current AFWA and WMO data will be beneficial particularly in areas where the AFWA and WMO data are sparse.  Therefore, the integration of the AMSR-E soil moisture product into the PECAD FAS DSS is envisaged to provide a better characterization of surface wetness conditions at the regional scale and enable more accurate monitoring of boundary condition changes in key agricultural areas.


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