Estimating time-dependent vegetation biases in the SMAP soil moisture product
<p>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 co...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-08-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/22/4473/2018/hess-22-4473-2018.pdf |
| _version_ | 1818432540457828352 |
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| author | S. Zwieback S. Zwieback A. Colliander M. H. Cosh J. Martínez-Fernández H. McNairn P. J. Starks M. Thibeault A. Berg |
| author_facet | S. Zwieback S. Zwieback A. Colliander M. H. Cosh J. Martínez-Fernández H. McNairn P. J. Starks M. Thibeault A. Berg |
| author_sort | S. Zwieback |
| collection | DOAJ |
| description | <p>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 <i>R</i><sup>2</sup> 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.</p> |
| first_indexed | 2024-12-14T16:06:49Z |
| format | Article |
| id | doaj.art-707a515db0f94f7894a7e078e0ddd841 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-14T16:06:49Z |
| publishDate | 2018-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-707a515db0f94f7894a7e078e0ddd8412022-12-21T22:55:04ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382018-08-01224473448910.5194/hess-22-4473-2018Estimating time-dependent vegetation biases in the SMAP soil moisture productS. Zwieback0S. Zwieback1A. Colliander2M. H. Cosh3J. Martínez-Fernández4H. McNairn5P. J. Starks6M. Thibeault7A. Berg8Department of Geography, University of Guelph, Guelph, Ontario, CanadaDepartment of Environmental Engineering, ETH Zurich, Zurich, SwitzerlandNASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USAUSDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, Maryland, USAInstituto Hispano Luso de Investigaciones Agrarias, Universidad de Salamanca, Salamanca, SpainScience and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, Ontario, CanadaUSDA-ARS Grazinglands Research Laboratory, El Reno, Oklahoma, USAComisión Nacional de Actividades Espaciales, Buenos Aires, ArgentinaDepartment of Geography, University of Guelph, Guelph, Ontario, Canada<p>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 <i>R</i><sup>2</sup> 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.</p>https://www.hydrol-earth-syst-sci.net/22/4473/2018/hess-22-4473-2018.pdf |
| spellingShingle | S. Zwieback S. Zwieback A. Colliander M. H. Cosh J. Martínez-Fernández H. McNairn P. J. Starks M. Thibeault A. Berg Estimating time-dependent vegetation biases in the SMAP soil moisture product Hydrology and Earth System Sciences |
| title | Estimating time-dependent vegetation biases in the SMAP soil moisture product |
| title_full | Estimating time-dependent vegetation biases in the SMAP soil moisture product |
| title_fullStr | Estimating time-dependent vegetation biases in the SMAP soil moisture product |
| title_full_unstemmed | Estimating time-dependent vegetation biases in the SMAP soil moisture product |
| title_short | Estimating time-dependent vegetation biases in the SMAP soil moisture product |
| title_sort | estimating time dependent vegetation biases in the smap soil moisture product |
| url | https://www.hydrol-earth-syst-sci.net/22/4473/2018/hess-22-4473-2018.pdf |
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