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| Titel |
Error characterisation of global active and passive microwave soil moisture datasets |
| VerfasserIn |
W. A. Dorigo, K. Scipal, R. M. Parinussa, Y. Y. Liu, W. Wagner, R. A. M. Jeu, V. Naeimi |
| 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 ; 14, no. 12 ; Nr. 14, no. 12 (2010-12-16), S.2605-2616 |
| Datensatznummer |
250012534
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| Publikation (Nr.) |
 copernicus.org/hess-14-2605-2010.pdf |
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| Zusammenfassung |
| Understanding the error structures of remotely sensed soil moisture
observations is essential for correctly interpreting observed variations and
trends in the data or assimilating them in hydrological or numerical weather
prediction models. Nevertheless, a spatially coherent assessment of the
quality of the various globally available datasets is often hampered by the
limited availability over space and time of reliable in-situ measurements. As an alternative, this study explores the triple collocation error estimation
technique for assessing the relative quality of several globally available
soil moisture products from active (ASCAT) and passive (AMSR-E and SSM/I)
microwave sensors. The triple collocation is a powerful statistical tool to
estimate the root mean square error while simultaneously solving for
systematic differences in the climatologies of a set of three linearly
related data sources with independent error structures. Prerequisite for
this technique is the availability of a sufficiently large number of timely
corresponding observations. In addition to the active and passive
satellite-based datasets, we used the ERA-Interim and GLDAS-NOAH reanalysis
soil moisture datasets as a third, independent reference. The prime
objective is to reveal trends in uncertainty related to different
observation principles (passive versus active), the use of different
frequencies (C-, X-, and Ku-band) for passive microwave observations, and
the choice of the independent reference dataset (ERA-Interim versus
GLDAS-NOAH).
The results suggest that the triple collocation method provides realistic
error estimates. Observed spatial trends agree well with the existing theory
and studies on the performance of different observation principles and
frequencies with respect to land cover and vegetation density. In addition,
if all theoretical prerequisites are fulfilled (e.g. a sufficiently large
number of common observations is available and errors of the different
datasets are uncorrelated) the errors estimated for the remote sensing
products are hardly influenced by the choice of the third independent
dataset. The results obtained in this study can help us in developing
adequate strategies for the combined use of various scatterometer and
radiometer-based soil moisture datasets, e.g. for improved flood forecast
modelling or the generation of superior multi-mission long-term soil
moisture datasets. |
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