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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 #299020

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Evaluation of assumptions in soil moisture triple collocation analysis

item Yilmaz, M - Collaborator
item Crow, Wade

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/15/2014
Publication Date: 6/1/2014
Publication URL:
Citation: Yilmaz, M.T., Crow, W.T. 2014. Evaluation of assumptions in soil moisture triple collocation analysis. Journal of Hydrometeorology. 15:1293-1302. DOI:10.1175/JHM-D-0158.1

Interpretive Summary: Accurate monitoring of soil moisture conditions is important for a range of agricultural applications including drought monitoring, fertilizer scheduling and irrigation management. The best available techniques for estimating soil moisture patterns over large geographic areas are based on the merging of information acquired from several independent techniques for measuring (and/or modeling) soil moisture. Such merging techniques require accurate information concerning the relative accuracy of each individual soil moisture measurement approach (so that more weight can be applied to relatively better estimates during the merging process). Recently a statistical technique called "Triple Collocation Analysis" has been developed which claims to provide such accuracy information. However, the approach is based on a set of statistical assumptions that have not been independently verified. This paper represents the first attempt to verify the statistical assumption required by Triple Collocation Analysis to provide unbiased estimates of errors in various large-scale soil moisture products. Once properly verified, Triple Collocation Analysis will provide a powerful tool for improving the estimation of large-scale soil moisture patterns over agricultural landscapes.

Technical Abstract: Triple collocation analysis (TCA) enables estimation of error variances for three or more products that retrieve or estimate the same geophysical variable using mutually-independent methods. Several statistical assumptions regarding the statistical nature of errors (e.g., mutual independence and orthogonality with respect to the truth) are required for TCA estimates to be unbiased. Even though soil moisture studies commonly acknowledge that these assumptions are required for an un-biased TCA, no study has specifically investigated the degree to which errors in existing soil moisture datasets conform to these assumptions. Here, we evaluate these assumptions both analytically and numerically over four extensively-instrumented watershed sites using soil moisture products derived from active microwave remote sensing, passive microwave remote sensing, and a land surface model. Results demonstrate that non-orthogonal and error-cross-covariance terms represent a significant fraction of the total variance of these products. However, the overall impact of error cross-correlation on TCA is found to be significantly larger than the impact of non-orthogonal errors. Due to the impact of cross-correlated errors, TCA error estimates generally underestimate the true random error of soil moisture products.