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Assimilation of a Satellite-Based SoilMoisture Product into a Two-Layer Water Balance Model for a Global Crop Production Decision Support System

  • John D. Bolten
  • Wade T. Crow
  • Xiwu Zhan
  • Curt A. Reynolds
  • Thomas J. Jackson

Abstract

Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global crop conditions. Soil moisture observations are particularly important for crop yield fluctuation forecasts provided by the US Department of Agriculture’s (USDA) International Production Assessment Division (IPAD) of the Office of Global Analysis (OGA) within the Foreign Agricultural Service (FAS). The current system utilized by IPAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). 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. However, many regions of the globe lack observations at the temporal and spatial resolutions required by IPAD. This study incorporates NASA’s soil moisture remote sensing product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) to the U.S. Department of Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational data assimilation system has been designed and implemented to provide CADRE with a daily soil moisture analysis product obtained via the assimilation of AMSR-E surface soil moisture retrievals into the IPAD two-layer soil moisture model. This chapter presents a methodology of data assimilation system design and a brief evaluation of the system performance over the Conterminous United States (CONUS).

Keywords

Soil Moisture Advance Very High Resolution Radiometer Advance Very High Resolution Radiometer Surface Soil Moisture Soil Moisture Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • John D. Bolten
    • 1
  • Wade T. Crow
  • Xiwu Zhan
  • Curt A. Reynolds
  • Thomas J. Jackson
  1. 1.USDA-ARS Hydrology and Remotes Sensing LabBeltsville

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