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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #297283
Title: Evaluating the performance of a soil moisture data assimilation system for agricultural drought monitoring

Author
item HAN, E - Collaborator
item Crow, Wade
item Holmes, Thomas
item BOLTEN, J - National Aeronautics And Space Administration (NASA)

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 10/21/2013
Publication Date: 12/9/2013
Citation: Han, E., Crow, W.T., Holmes, T.R., Bolten, J. 2013. Evaluating the performance of a soil moisture data assimilation system for agricultural drought monitoring [abstract]. Proceedings of the 2013 Fall American Geophysical Union, December 9-13, 2013, San Francisco, CA. 2013 CD ROM.

Interpretive Summary:

Technical Abstract: Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, we evaluate a soil moisture assimilation system for agricultural drought monitoring by benchmarking each component of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model and a sequential data assimilation filter) against a series of linear models which perform the same function (i.e., have the same basic inputs/output) as the full component. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which non-linearities and/or complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model and an Ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system. First, the non-linear LPRM retrieval algorithm does not appear to add much additional predictive information for future NDVI compared to the simple linear benchmark model comprised of initial AMSR-E observations (horizontally and vertically polarized brightness temperatures and surface temperature). Second, the Palmer model performed worse than the purely linear prognostic model (Antecedent Precipitation Index model) in predicting future vegetation condition. This result points out that the saturation threshold of soil layers in the modern LSMs for runoff generation hinders maximum utilization of meteorological input information for agricultural drought monitoring. As to the assimilation algorithm, better performance of the benchmark model than the EnKF proves that inappropriate model and observation error assumption can result in sub-optimal filter operations. The benchmarking evaluation of the current soil moisture data assimilation system shows that the most predictive skills of the assimilated soil moisture estimates are attributed to the initial AMSR-E observations rather than the non-linearities and/or complexities in the algorithm and models.