Enhancing the USDA Global Crop Production Decision Support System with Nasa Land Information System and Water Cycle Satellite Observations
Hydrology and Remote Sensing Laboratory
2011 Annual Report
1a.Objectives (from AD-416)
Enhance USDA FAS crop yield forecasts via the use of remote sensing and data assimilation technologies.
1b.Approach (from AD-416)
Apply an Ensemble Kalman filter to the assimilation of AMSR-E soil moisture retrievals into the USDA FAS land surface model. Development contingency plans for the discontinuity of AMSR-E data and apply the NASA LIS modeling system.
We have completed development of an independent verification system which USDA FAS can use to justify the added benefits of ingesting NASA soil moisture data products into their decision support system. Co-investigators have completed development of a soil moisture data product system (SMOPS) at NOAA NESDIS to operationally produce a surface soil moisture product using satellite brightness temperature data acquired from a variety of satellite sources. Consequently, SMOPS provides a contingency in case access to AMSR-E data products is lost. Access to all three contingency satellites (SMOS, MWIR and Windsat) has been established. In addition, we have established and maintained operational delivery of soil moisture products (both an AMSR-E satellite-only surface soil moisture product and surface and root-zone soil moisture products created in a data assimilation system) to USDA FAS. USDA FAS is currently in the process of ingesting these products into their operational system.