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| Titel |
Multi-scale analysis of bias correction of soil moisture |
| VerfasserIn |
C.-H. Su, D. Ryu |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 1 ; Nr. 19, no. 1 (2015-01-06), S.17-31 |
| Datensatznummer |
250120579
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| Publikation (Nr.) |
 copernicus.org/hess-19-17-2015.pdf |
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| Zusammenfassung |
| Remote sensing, in situ networks and models are now providing unprecedented
information for environmental monitoring. To conjunctively use multi-source
data nominally representing an identical variable, one must resolve biases
existing between these disparate sources, and the characteristics of the
biases can be non-trivial due to spatio-temporal variability of the target
variable, inter-sensor differences with variable measurement supports. One
such example is of soil moisture (SM) monitoring. Triple collocation (TC)
based bias correction is a powerful statistical method that is increasingly
being used to address this issue, but is only applicable to the linear
regime, whereas the non-linear method of statistical moment matching is
susceptible to unintended biases originating from measurement error. Since
different physical processes that influence SM dynamics may be
distinguishable by their characteristic spatio-temporal scales, we propose a
multi-timescale linear bias model in the framework of a wavelet-based
multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to
demonstrate scale-dependent biases between in situ, remotely sensed and
modelled SM, the influence of various prospective bias correction schemes on
these biases, and lastly to enable multi-scale bias correction and
data-adaptive, non-linear de-noising via wavelet thresholding. |
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