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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: The contribution of soil moisture retrievals to land data assimilation products

Authors
item Reichle, Rolf - NASA GSFC
item Crow, Wade
item Koster, R - NASA GSFC
item Sharif, H - UNIVERSITY OF TEXAS
item Mahanama, S - NASA GSFC

Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 26, 2007
Publication Date: January 10, 2008
Citation: Reichle, R.H., Crow, W.T., Koster, R.D., Sharif, H.O., Mahanama, S.P. 2008. The contribution of soil moisture retrievals to land data assimilation products. Geophysical Research Letters. 35, L01404, http://dx.doi.org/10.1029/2007GL031986.

Interpretive Summary: Data assimilation systems optimally combine land surface information derived from both remote sensing observations and land surface models (driven primarily using rainfall observations). Consequently the added value of remotely sensed observations for agricultural applications depends not just on the accuracy of the observations themselves but also on the accuracy of competing information derived from land surface models alone. This is critical because if land surface models alone can provide critical information than there is no need to invest is expensive remote sensing technologies. This study presents a new methodology for determining the added value associated with remote sensing soil moisture observations for a wide variety of potential observation and modeling accuracies. As such, it provides a more rigorous description of the added benefits associated with deploying satellite-base soil moisture observing systems.

Technical Abstract: Satellite retrievals of surface soil moisture are subject to errors and cannot provide complete space-time coverage. Data assimilation systems merge available satellite retrievals with information from land surface models and antecedent meteorological data, information that is spatio-temporally complete but likewise uncertain. For the design of new satellite missions it is critical to understand just how uncertain satellite retrievals can be and still be useful. Here, we present a synthetic data assimilation experiment that determines the contribution of satellite retrievals to the skill of land assimilation products as a function of retrieval and land model skill. As expected, the skill of the assimilation products increases with both the skill of the model and that of the retrievals. The skill of the soil moisture assimilation products always exceeds that of the model acting alone; even retrievals of low quality contribute information to the assimilation product, particularly if model skill is modest.

Last Modified: 9/22/2014
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