A geostatistical approach to upscale soil moisture with unequal precision observations

Wang, J.; Ge, Y.; Song, Yongze; Li, X.

doi:10.1109/LGRS.2014.2321429

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Authors

Wang, J.

Ge, Y.

Song, Yongze

Li, X.

Date

2014

Type

Journal Article

Metadata

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Citation

Wang, J. and Ge, Y. and Song, Y. and Li, X. 2014. A geostatistical approach to upscale soil moisture with unequal precision observations. IEEE Geoscience and Remote Sensing Letters. 11 (12): pp. 2125-2129.

Source Title

IEEE Geoscience and Remote Sensing Letters

DOI

10.1109/LGRS.2014.2321429

ISSN

1545-598X

Faculty

Faculty of Humanities

School

School of Design and the Built Environment

URI

http://hdl.handle.net/20.500.11937/77048

Collection

Curtin Research Publications

Abstract

Upscaling ground-based moisture observations to satellite footprint-scale estimates is an important problem in remote sensing soil-moisture product validation. The reliability of validation is sensitive to the quality of input observation data and the upscaling strategy. This letter proposes a model-based geostatistical approach to scale up soil moisture with observations of unequal precision. It incorporates unequal precision in the spatial covariance structure and uses Monte Carlo simulation in combination with a block kriging (BK) upscaling strategy. The approach is illustrated with a real-world application for upscaling soil moisture in the Heihe Watershed Allied Telemetry Experimental Research experiment. The results show that BK with unequal precision observations can consider both random ground-based measurement errors and upscaling model error to achieve more reliable estimates. We conclude that this approach is appropriate to quantify upscaling uncertainties and to investigate the error propagation process in soil-moisture upscaling. © 2004-2012 IEEE.