气候变化领域集成服务门户(CCPortal): Estimating time-dependent vegetation biases in the SMAP soil moisture product

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DOI	10.5194/hess-22-4473-2018
	Estimating time-dependent vegetation biases in the SMAP soil moisture product
	Zwieback S.; Colliander A.; Cosh M.H.; Martínez-Fernández J.; McNairn H.; Starks P.J.; Thibeault M.; Berg A.
发表日期	2018
ISSN	1027-5606
起始页码	4473
结束页码	4489
卷号	22 期号:8
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.
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
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159937
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
推荐引用方式 GB/T 7714	Zwieback S.,Colliander A.,Cosh M.H.,et al. Estimating time-dependent vegetation biases in the SMAP soil moisture product[J],2018,22(8).
APA	Zwieback S..,Colliander A..,Cosh M.H..,Martínez-Fernández J..,McNairn H..,...&Berg A..(2018).Estimating time-dependent vegetation biases in the SMAP soil moisture product.Hydrology and Earth System Sciences,22(8).
MLA	Zwieback S.,et al."Estimating time-dependent vegetation biases in the SMAP soil moisture product".Hydrology and Earth System Sciences 22.8(2018).