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United States Department of Agriculture

Agricultural Research Service

Title: Assimilation of AMSR-E soil moisture retrievals into the USDA Global Crop Production Decision Support System

item Bolten, John
item Crow, Wade
item Zhan, Xiwu
item Jackson, Thomas
item Reynolds, Curt
item Doorn, Brad

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 11/5/2006
Publication Date: 11/5/2006
Citation: Bolten, J.D., Crow, W.T., Zhan, X., Jackson, T.J., Reynolds, C., Doorn, B. 2006. Assimilation of AMSR-E soil moisture retrievals into the USDA global crop production decision support team [abstract]. EOS Transactions, American Geophysical Union, Fall Supplements. 87(52):H23E-1550.

Interpretive Summary:

Technical Abstract: Soil moisture is a fundamental data source used in crop growth stage and crop stress models. The accuracy of these models is highly dependent upon the data sources used; particularly the accuracy, consistency, and spatial and temporal coverage of the land and climatic forcing data. The U. S. Department of Agriculture (USDA) Production Estimates and Crop Assessment Division (PECAD) state of the art soil moisture inputs are taken from a merging of point observations and secondary model outputs. However, these model inputs do not fully satisfy the spatial and temporal scales necessary for optimal global agriculture assessment. The PECAD Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System (DSS) utilizes a modification of the Palmer two-layer soil moisture model to estimate surface soil moisture. Inputs into this model include 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. This study aims at improving the PECAD soil moisture estimates by integrating remotely sensed soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E) instrument into the USDA CADRE DSS. Estimates of soil moisture are calculated in a 1-D Ensemble Kalman Filter (EnKF) using climatologically re-scaled AMSR-E soil moisture retrievals and USDA water balance model soil moisture estimates. The scaling methodology, assimilation framework, and preliminary results will be presented.

Last Modified: 07/25/2017
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