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United States Department of Agriculture

Agricultural Research Service

Title: The integration of remotely sensed soil moisture into the USDA global crop production support system

item Bolten, John
item Crow, Wade
item Zhan, Xiwu
item Jackson, Thomas
item Reynolds, C

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 4/1/2007
Publication Date: 5/1/2007
Citation: Bolten, J.D., Crow, W.T. Zhan, X., Jackson, T.J., Reynolds, C.A. 2007. The integration of remotely sensed soil moisture into the USDA global crop production support system [abstract]. Abs. 3, BARC Poster Day.

Interpretive Summary:

Technical Abstract: Soil moisture is a fundamental data source used in crop growth stage and crop stress models. Currently, the USDA Production Estimates and Crop Assessment Division (PECAD) utilizes a modification of the Palmer two-layer soil moisture model to estimate surface soil moisture. This model uses a simplified water balance scheme based on inputs of soil parameter values of soil water holding capacity, daily precipitation and temperature estimates provided by 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 remotely sensed soil moisture observations. The integration of ASMR-E soil moisture estimates with the existing PECAD database is envisaged to provide a better characterization of surface wetness at the regional scale and enable more accurate monitoring of boundary condition changes in key agricultural areas. We assess the performance of the Ensemble Kalman Filter (EnKF) for the assimilation of soil moisture estimates from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the PECAD soil moisture model. The feasibility of the approach is analyzed within a series of synthetic experiments implementing various ensemble sizes and forecast errors. The benefit of utilizing AMSR-E soil moisture over PECAD data-poor regions is assessed by assimilating AMSR-E values over areas of synthetically degraded precipitation. An overview of the soil moisture model, assimilation algorithm, spatial and temporal scaling techniques, and methods of accuracy assessment will be presented.

Last Modified: 10/17/2017
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