|Bolten, J -|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: June 15, 2012
Publication Date: July 22, 2012
Citation: Crow, W.T., Bolten, J. 2012. Improved forecasting of global vegetation conditions using remotely-sensed surface soil moisture[abstract]. International Geoscience and Remote Sensing Symposium. 2012 CDROM. Technical Abstract: Timely and accurate monitoring of anomalies in root-zone soil water availability is essential for assessing global agricultural crop conditions. Root-zone soil moisture estimates are particularly important for obtaining forecasts of end-of-season crop yield fluctuations provided by the United States Department of Agriculture’s (USDA) Production Estimation and Crop Assessment Division (PECAD). The current system utilized by USDA FAS/PECAD estimates soil moisture from a 2-layer water balance model (referred hereinafter as the “Palmer 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. Consequently, in data-poor regions of the world, root-zone soil moisture estimates derived from this system are substantially degraded by random errors associated with inadequate ground sampling of input precipitation. The assimilation of remotely-sensed surface soil moisture retrievals has been proposed as a means to address this shortcoming. In particular, this research assimilates soil moisture remote sensing products derived from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) and the European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) into the existing USDA FAS root-zone soil moisture monitoring system. This proposed data assimilation analysis system will then be evaluated based on its ability to increase the lagged correlation of Palmer model root-zone soil moisture predictions with subsequent variations in remotely-sensed vegetation indices. Such improves provide clear evidence that the quality of existing USDA FAS root-zone soil moisture products are being enhanced by the inclusion of remotely-sensed soil moisture products.