Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 7/15/2008
Publication Date: 8/1/2008
Citation: Bolten, J.D., Crow, W.T., Zhan, X. Jackson, T.J., Reynolds, C.A. 2008. Integration of satellite-retrieved soil moisture observations with a global two-layer soil moisture model. In: Proceedings of the International Geoscience and Remote Sensing Symposium, July 7-11, 2008, Boston, Masschusetts. 2008 CDROM. Interpretive Summary:
Technical Abstract: The U.S. Department of Agriculture (USDA) Production Estimates and Crop Assessment Division (PECAD) is responsible for providing monthly global crop estimates that heavily influence global commodity market access. These estimates are derived from a merging of many data sources including satellite and ground observations, and more than 20 years of climatology and crop behavior data over key agricultural areas. To most efficiently manage the data sources, PECAD has developed a series of analytical tools, crop models, and hazard calendars within a Crop Condition Data Retrieval and Evaluation (CADRE) Data Base Management System (DBMS). The goal of CADRE is to provide timely and accurate estimates of global crop conditions for use in up-to-date commodity intelligence reports. A crucial requirement of these global crop yield forecasts is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and soil wetness, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult . Temporal resolution is particularly important for predicting adequate surface wetting and drying between precipitation events and is closely integrated with CADRE. This work aims at improving the PECAD surface and sub-surface soil moisture estimates by merging satellite-retrieved soil moisture estimates with the current two-layer soil moisture model used within the DBMS. The improved temporal resolution and spatial coverage of the satellite-based EOS Advanced Microwave Scanning Radiometer (AMSR-E) is envisaged to provide a better characterization of surface wetness at the regional scale and enable more accurate crop monitoring in key agricultural areas.