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| Titel |
Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes |
| VerfasserIn |
S. V. Kumar, C. D. Peters-Lidard, J. A. Santanello, R. H. Reichle, C. S. Draper, R. D. Koster, G. Nearing, M. F. Jasinski |
| 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. 11 ; Nr. 19, no. 11 (2015-11-06), S.4463-4478 |
| Datensatznummer |
250120844
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| Publikation (Nr.) |
 copernicus.org/hess-19-4463-2015.pdf |
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| Zusammenfassung |
| Earth's land surface is characterized by tremendous natural heterogeneity
and human-engineered modifications, both of which are challenging to
represent in land surface models. Satellite remote sensing is often the most
practical and effective method to observe the land surface over large
geographical areas. Agricultural irrigation is an important human-induced
modification to natural land surface processes, as it is pervasive across
the world and because of its significant influence on the regional and global
water budgets. In this article, irrigation is used as an example of a human-engineered,
often unmodeled land surface process, and the utility of satellite soil
moisture retrievals over irrigated areas in the continental US is examined.
Such retrievals are based on passive or active microwave observations from
the Advanced Microwave Scanning Radiometer for the Earth Observing System
(AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil
Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced
Scatterometer (ASCAT). The analysis suggests that the skill of these
retrievals for representing irrigation effects is mixed, with ASCAT-based
products somewhat more skillful than SMOS and AMSR2 products. The article
then examines the suitability of typical bias correction strategies in
current land data assimilation systems when unmodeled processes dominate the
bias between the model and the observations. Using a suite of synthetic
experiments that includes bias correction strategies such as quantile mapping
and trained forward modeling, it is demonstrated that the bias correction
practices lead to the exclusion of the signals from unmodeled processes, if
these processes are the major source of the biases. It is further shown that
new methods are needed to preserve the observational information about
unmodeled processes during data assimilation. |
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