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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 #317569
Title: The potential of 2D Kalman filtering for soil moisture data assimilation

Author
item GRUBER, ALEX - Vienna University Of Technology
item Crow, Wade
item DORIGO, W.A. - Vienna University Of Technology
item WAGNER, W. - Vienna University Of Technology

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/1/2015
Publication Date: 12/15/2015
Citation: Gruber, A., Crow, W.T., Dorigo, W., Wagner, W. 2015. The potential of 2D Kalman filtering for soil moisture data assimilation. Remote Sensing of Environment. 171:137-148.

Interpretive Summary: Agricultural drought has enormous consequences for domestic economic interests and international food security concerns. These implications can be minimized by the effective detection and mitigation of drought impacts. Land data assimilation systems are increasingly being tasked with optimally combining hydrologic information acquired from a variety of sources (e.g., hydrologic modeling, ground-based observations and remotely-sensed observations) into integrated predictions of root-zone soil moisture availability. These predictions, in turn, form the basis of agricultural drought monitoring systems. The performance of these systems requires detailed information about errors in various sources of soil moisture information (since accurate products with low errors should be weighted more than inaccurate products with high errors). This paper describes a new mathematical technique for deriving soil moisture error information required for data assimilation systems to accurately predict the onset and evolution of drought. In particular, it provides a means for better estimating the statistical spatial structure of modeling and remote sensing errors. The insight gained from this analysis will eventually be used to improve the ability of operational USDA agencies (e.g. FAS and NASS) to monitor the onset, duration and severity of agricultural drought.

Technical Abstract: We examine the potential for parameterizing a two-dimensional (2D) land data assimilation system using spatial error auto-correlation statistics gleaned from a triple collocation analysis and the triplet of: (1) active microwave-, (2) passive microwave- and (3) land surface model-based surface soil moisture products. Results demonstrate that, while considerable spatial error auto-correlation exists in the errors for all three products, the inclusion of this information into a 2D assimilation system does not signi cantly improve the performance of the system relative to a 1D baseline. This result is explained via an analytical evaluation of the impact of spatial error auto-correlation on the steady-state Kalman gain, which reveals that 2D filtering requires the existence of large auto-correlation di erences (between the assimilation model andthe assimilated observations) in order to enhance the analysis relative to a 1D filtering baseline As a result, large error auto-correlations alone (in both the model or observations) are not sucient to motivate the application of a 2D land assimilation system. These results have important consequences for the development of land data assimilation systems designed to ingest satellite derived surface soil moisture products for water resource and climate applications.