globalchange  > 气候变化与战略
DOI: 10.5194/hess-22-4473-2018
论文题名:
Estimating time-dependent vegetation biases in the SMAP soil moisture product
作者: Zwieback S.; Colliander A.; Cosh M.H.; Martínez-Fernández J.; McNairn H.; Starks P.J.; Thibeault M.; Berg A.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期:8
起始页码: 4473
结束页码: 4489
语种: 英语
Scopus关键词: Sensitivity analysis ; Soil surveys ; Systematic errors ; Vegetation ; Characteristic fingerprints ; Explanatory variables ; Modelling studies ; Potential sources ; Remotely sensed soil moisture ; Seasonal variation ; Soil moisture active passive (SMAP) ; Soil moisture dynamics ; Soil moisture ; agricultural land ; biomass ; estimation method ; extreme event ; remote sensing ; seasonal variation ; soil moisture ; soil-vegetation interaction ; vegetation cover
英文摘要: 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. © Author(s) 2018.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163220
Appears in Collections:气候变化与战略

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作者单位: Zwieback, S., Department of Geography, University of Guelph, Guelph, ON, Canada, Department of Environmental Engineering, ETH Zurich, Zurich, Switzerland; Colliander, A., NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; Cosh, M.H., USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States; Martínez-Fernández, J., Instituto Hispano Luso de Investigaciones Agrarias, Universidad de Salamanca, Salamanca, Spain; McNairn, H., Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON, Canada; Starks, P.J., USDA-ARS Grazinglands Research Laboratory, El Reno, OK, United States; Thibeault, M., Comisión Nacional de Actividades Espaciales, Buenos Aires, Argentina; Berg, A., Department of Geography, University of Guelph, Guelph, ON, Canada

Recommended Citation:
Zwieback S.,Colliander A.,Cosh M.H.,et al. Estimating time-dependent vegetation biases in the SMAP soil moisture product[J]. Hydrology and Earth System Sciences,2018-01-01,22(8)
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