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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 #353597

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

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

item ZWIEBACK, S. - University Of Guelph
item COLLIANDER, A. - Jet Propulsion Laboratory
item Cosh, Michael
item MARTINEZ, FERNANDEZ,J. - University Of Salamanca
item MCNAIRN, H. - Agriculture And Agri-Food Canada
item Starks, Patrick - Pat
item THIBEAULT, M. - Universidad De Buenos Aires
item BERG, A. - University Of Guelph

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/22/2018
Publication Date: 8/22/2018
Citation: Zwieback, S., Colliander, A., Cosh, M.H., Martinez, F., McNairn, H., Starks, P.J., Thibeault, M., Berg, A. 2018. Estimating time-dependent vegetation biases in the SMAP soil moisture product. Hydrology and Earth System Sciences. 22(8):4473-4489.

Interpretive Summary: Soil moisture remote sensing is influenced by vegetation, and algorithms used to estimate soil moisture can incorporate vegetation estimates in different ways. A consistent seasonal correction for vegetation can be developed by using Bayesian statistical techniques which require little additional information other than long term patterns within the microwave signal. Until such time that direct vegetation parameterization can be developed, this Bayesian methodology can quickly identify the potential domains of bias from vegetation influence. This study will benefit algorithm development and the modeling community for hydrologic and ecosystem studies.

Technical 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 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 are distorted, as R2 values with a biomass proxy increase by 0.1 on average compared to in-situ data. We conclude that complex biases can be present in soil moisture products and that they should be accounted for in observational and modelling studies.