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Title: Evaluating Remotely-Sensed Surface Soil Moisture Estimates Using Triple Collocation

Author
item Crow, Wade
item MIRRALLES, DIEGO - Collaborator
item Cosh, Michael

Submitted to: International Association of Hydrological Science
Publication Type: Proceedings
Publication Acceptance Date: 11/1/2010
Publication Date: N/A
Citation: N/A

Interpretive Summary: Satellite-based surface soil moisture estimates offer the potential to aid in a variety of key agricultural water resource applications including drought monitoring, irrigation scheduling and accurate rainfall prediction. However, difficulties associated with evaluating satellite soil moisture estimates form a major technical barrier to their successful implementation in these areas. This paper applies a novel statistical technique to estimate error in remotely-sensed soil moisture obtained from passive microwave observations via comparisons with coincident (and equally uncertain) soil moisture products derived from land surface modeling and active microwave (i.e. radar) remote sensing. Application of the approach should improve our ability to evaluate (and subsequently improve) remotely-sensed surface soil moisture retrievals and therefore enhance prospects for developing viable agricultural applications for these observations.

Technical Abstract: Recent work has demonstrated the potential of enhancing remotely-sensed surface soil moisture validation activities through the application of triple collocation techniques which compare time series of three mutually independent geophysical variable estimates in order to acquire the root-mean-square error of each estimate individually. Using soil moisture observations obtained within four intensively instrumented watersheds, this analysis provides the first independent verification of a triple collocation technique applied to soil moisture estimates acquired from: land surface modeling, passive microwave remote sensing and active microwave remote sensing. Results demonstrate the potential for validating remote-sensed soil moisture products using the approach and identify a possible source of bias associated with cross-correlated errors in remotely-sensed soil moisture products.