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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 8 | Copyright
Hydrol. Earth Syst. Sci., 22, 4473-4489, 2018
https://doi.org/10.5194/hess-22-4473-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 22 Aug 2018

Research article | 22 Aug 2018

Estimating time-dependent vegetation biases in the SMAP soil moisture product

Simon Zwieback1,2, Andreas Colliander3, Michael H. Cosh4, José Martínez-Fernández5, Heather McNairn6, Patrick J. Starks7, Marc Thibeault8, and Aaron Berg1 Simon Zwieback et al.
  • 1Department of Geography, University of Guelph, Guelph, Ontario, Canada
  • 2Department of Environmental Engineering, ETH Zurich, Zurich, Switzerland
  • 3NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
  • 4USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, Maryland, USA
  • 5Instituto Hispano Luso de Investigaciones Agrarias, Universidad de Salamanca, Salamanca, Spain
  • 6Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada
  • 7USDA-ARS Grazinglands Research Laboratory, El Reno, Oklahoma, USA
  • 8Comisión Nacional de Actividades Espaciales, Buenos Aires, Argentina

Abstract. Remotely sensed soil moisture products are influenced by vegetation and how it is accounted for in the retrieval, which is a potential source of time-variable biases. To estimate such complex, time-variable error structures from noisy data, we introduce a Bayesian extension to triple collocation in which the systematic errors and noise terms are not constant but vary with explanatory variables. We apply the technique to the Soil Moisture Active Passive (SMAP) soil moisture product over croplands, hypothesizing that errors in the vegetation correction during the retrieval leave a characteristic fingerprint in the soil moisture time series. We find that time-variable offsets and sensitivities are commonly associated with an imperfect vegetation correction. Especially the changes in sensitivity can be large, with seasonal variations of up to 40%. Variations of this size impede the seasonal comparison of soil moisture dynamics and the detection of extreme events. Also, estimates of vegetation–hydrology coupling can be distorted, as the SMAP soil moisture has larger R2 values with a biomass proxy than the in situ data, whereas noise alone would induce the opposite effect. This observation highlights that time-variable biases can easily give rise to distorted results and misleading interpretations. They should hence be accounted for in observational and modelling studies.

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Short summary
Satellite soil moisture products can provide critical information on incipient droughts and the interplay between vegetation and water availability. However, time-variant systematic errors in the soil moisture products may impede their usefulness. Using a novel statistical approach, we detect such errors (associated with changing vegetation) in the SMAP soil moisture product. The vegetation-associated biases impede drought detection and the quantification of vegetation–water interactions.
Satellite soil moisture products can provide critical information on incipient droughts and the...
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