Improved Global Agricultural Assessment and Forecasting from AMSR-E Soil Moisture Estimates
John
Bolten, USDA-ARS Hydrology and Remote Sensing Lab, jbolten@hydrolab.arsusda.gov
(Presenting)
Wade
Crow, USDA-ARS Hydrology and Remote Sensing Lab, wcrow@hydrolab.arsusda.gov
Xiwu
Zhan, USDA-ARS Hydrology and Remote Sensing Lab, zhan@hydrolab.arsusda.gov
Tom
Jackson, USDA-ARS Hydrology and Remote Sensing Lab, tjackson@hydrolab.arsusda.gov
Curt
Reynolds, USDA-FAS Production Estimates and Crop Assessment Division, curt.reynolds@FAS.USDA.GOV
Brad
Doorn, USDA-FAS Production Estimates and Crop Assessment Division, brad.doorn@FAS.USDA.GOV
The United States Department of Agriculture (USDA) Production Estimates and Crop Assessment Division (PECAD) monitors and predicts global food supplies through the combination of meteorological, remote sensing, crop model and soil moisture model data.
These assessments are provided by the PECAD Crop Assessment Data Retrieval and Evaluation (CADRE) Data Base Management System (DBMS). Of particular importance to the timely and accurate estimation of crop yield forecasts in high impact areas is the frequent regional characterization of near surface soil moisture. In the past, reliable soil moisture estimates used by CADRE have been calculated from daily precipitation and temperature extremes at point locations. For over four years, the NASA EOS Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite has provided daily global soil moisture products. There are several alternative retrieval methods that are undergoing validation. The current research presents a methodology for integrating direct soil moisture observations from AMSR-E into the USDA CADRE to improve the predictive capability of PECAD crop forecasting capability. This presentation will give an overview of the current soil moisture model, the Ensemble Kalman Filter data assimilation algorithm, and expected results of the assimilated soil moisture data accuracy assessment. Comparisons of the AMSR-E and USDA calculated soil moisture datasets will also be presented.