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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 #292608
Title: Benchmarking 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: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 4/15/2013
Publication Date: 4/18/2013
Citation: Han, E., Crow, W.T., Holmes, T.R., Bolten, J. 2013. Benchmarking a soil moisture data assimilation system for agricultural drought monitoring [abstarct]. 2013 BARC Poster Day, April 18, 2013, Beltsville, MD.

Interpretive Summary:

Technical Abstract: Agricultural drought is defined as a shortage of moisture in the root zone of plants. Recently available satellite-based remote sensing data have accelerated development of drought early warning system by providing spatially continuous soil moisture information repeatedly at short-term interval. Nonetheless, shallow sensing depth (top few cm) of currently available satellite soil moisture retrievals necessitated integrating hydrologic models and surface soil moisture observations through data assimilation techniques to obtain more accurate root zone soil moisture estimates. Although a number of previous studies have demonstrated benefits of soil moisture data assimilation system, relatively little is known about the relative merits of particular retrieval, modeling and/or data assimilation strategies. In particular, it remains unclear what level of complexity and/or nonlinearity is appropriate for each of these components. In this study, we attempt to assess individual components of a drought-monitoring soil moisture data assimilation system and benchmark the efficiency of these components relative to simpler retrieval, modeling and data integration strategies. In this way, we improve our understanding of skill contributed by various components of the system and, ultimately, pinpoint specific aspects of such systems to target for improvement. First, the efficiency of a retrieval algorithm, Land parameter Retrieval Model (LPRM) is evaluated using data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). Second, the two-layer Palmer water balance model being in operational use by the USDA - Foreign Agricultural Service is tested. Lastly, a well-proven data assimilation technique, Ensemble Kalman filter (EnKF) is evaluated. The metric to measure the performance of each process is the lagged rank correlation between the output of each component and the normalized difference vegetation index (NDVI). A simple statistical model, autoregressive model is used as benchmarks (minimal reference level) against which the performances of different components of assimilation system are evaluated. Interestingly, it is found that most of the benefits from the assimilation system to predict root zone soil moisture are attributed to the initial remote sensing observations (i.e., brightness temperature). The non-linearities in the retrieval algorithm (LPRM), hydrologic model (Palmer model) and the EnKF marginally contribute to predictive skills of the system, not as significantly as expected. This suggests that considerable improvement is necessary in those non-linear processes and the existing model complexities may not necessarily be required for effective agricultural drought monitoring.