|BOLTEN, JOHN - National Aeronautics And Space Administration (NASA)|
Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 4/20/2011
Publication Date: 5/15/2011
Citation: Crow, W.T., Bolten, J. 2011. Enhancing begetation productivity forecasting using remotely-sensed surface soil moisture retrievals. International Geoscience and Remote Sensing Symposium Proceedings. August 1-5, 2011. CDROM.
Technical Abstract: With the onset of data availability from the ESA Soil Moisture and Ocean Salinity (SMOS) mission (Kerr and Levine, 2008) and the expected 2015 launch of the NASA Soil Moisture Active and Passive (SMAP) mission (Entekhabi et al., 2010), the next five years should see a significant expansion in our ability to monitor surface soil moisture conditions from space. One potential application for such products is their integration into decision support systems which require real-time agricultural drought information to forecast subsequent impacts on ecosystem productivity and/or agricultural yields. The United States Department of Agriculture (USDA) Foreign Agricultural Service (FAS) currently maintains such a system to globally predict end-to-season crop yields based on an analysis of available mid-season weather and crop condition information. A key piece of information for such predictions is the amount of root-zone (surface to 1-meter) soil water available in agricultural production regions. Currently, it is unknown how much additive information remotely-sensed soil moisture retrievals provide above and beyond soil moisture estimates which can be obtained from available precipitation observations and simple water balance modeling. This research will present a methodology for globally evaluating the added value of remotely-sensed surface soil moisture retrievals for vegetation productivity forecasting. The approach will be based on analyzing the lagged correlation between root-zone soil moisture anomalies obtained from a water balance model and subsequent anomalies in vegetation condition (as described by visible/near-infrared vegetation indices). This lagged correlation will be analyzed both before and after the assimilation of remotely-sensed surface soil moisture retrievals into the water balance model using an Ensemble Kalman Filter (EnKF). In this way, the added forecasting value of the remotely-sensed surface soil moisture product can be isolated.