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Title: Enhancing agricultural forecasting using SMOS surface soil moisture retrievals

item Crow, Wade
item BOLTEN, JOHN - National Aeronautics And Space Administration (NASA)

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/15/2011
Publication Date: 9/1/2011
Citation: Crow, W.T., Bolten, J. 2011. Enhancing agricultural forecasting using SMOS surface soil moisture retrievals [abstract]. Proceedings of 2011 SMOS Science Workshop, September 27-29, 2011. Arles , France. 2011 CDROM

Interpretive Summary:

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 viable application for SMOS and SMAP soil moisture products is their integration into decision support systems requiring real-time agricultural drought information to forecast subsequent impacts on agricultural productivity and yields. The United States Department of Agriculture (USDA) Foreign Agricultural Service (FAS) currently maintains such a system to globally predict end-of-season crop yields based on an analysis of available mid-season weather and crop condition information. These forecasts are then used to forecast agricultural food market conditions and for the early detection of famine in countries with food security challenges. A key input into the USDA FAS system is root-zone (surface to 1-meter) soil water availability in agricultural production regions. For such applications, it is currently unknown how much added information satellite-based surface soil moisture retrievals provide above and beyond root-zone soil moisture estimates which can be obtained from available global precipitation products and water balance modeling. This presentation will describe a methodology for globally evaluating the added value of SMOS and AMSR-E soil moisture retrievals for vegetation and crop condition forecasting based on analyzing the lagged-correlation between root-zone soil moisture anomalies obtained from a water balance model and future anomalies in vegetation condition (as described by visible/near-infrared vegetation indices). Such lagged cross-correlations will be analyzed both before and after the assimilation of remotely-sensed surface soil moisture retrievals into a soil water balance model using an Ensemble Kalman Filter (EnKF) to isolate the added impact of remotely-sensed surface soil moisture retrievals on agricultural forecasting activities. Preliminary results show significant added skill associated with the assimilation of satellite-based soil moisture products over areas of world with significant food security concerns. That is, satellite-based soil moisture products can significantly enhance our ability to estimate future vegetation/crop status based on current soil moisture conditions. The impact of upgrading from AMSR-E to SMOS-based retrievals will be discussed as well as the potential role of vegetation canopy optical depth retrievals in such a system. Finally, prospects for integrating SMOS retrievals into the USDA FAS crop yield forecasting system will be described.