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| Titel |
Homogeneity testing of the global ESA CCI multi-satellite soil moisture climate data record |
| VerfasserIn |
Wolfgang Preimesberger, Chun-Hsu Su, Alexander Gruber, Wouter Dorigo |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250145801
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| Publikation (Nr.) |
 EGU/EGU2017-9772.pdf |
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| Zusammenfassung |
| ESA’s Climate Change Initiative (CCI) creates a global, long-term data record by merging
multiple available earth observation products with the goal to provide a product for climate
studies, trend analysis, and risk assessments. The blending of soil moisture (SM) time series
derived from different active and passive remote sensing instruments with varying sensor
characteristics, such as microwave frequency, signal polarization or radiometric accuracy,
could potentially lead to inhomogeneities in the merged long-term data series, undercutting
the usefulness of the product. To detect the spatio-temporal extent of contiguous periods
without inhomogeneities as well as subsequently minimizing their negative impact on the
data records, different relative homogeneity tests (namely Fligner-Killeen test of
homogeneity of variances and Wilcoxon rank-sums test) are implemented and tested on
the combined active-passive ESA CCI SM data set. Inhomogeneities are detected
by comparing the data against reference data from in-situ data from ISMN, and
model-based estimates from GLDAS-Noah and MERRA-Land. Inhomogeneity testing is
performed over the ESA CCI SM data time frame of 38 years (from 1978 to 2015), on a
global quarter-degree grid and with regard to six alterations in the combination of
observation systems used in the data blending process. This study describes and explains
observed variations in the spatial and temporal patterns of inhomogeneities in the
combined products. Besides we proposes methodologies for measuring and reducing the
impact of inhomogeneities on trends derived from the ESA CCI SM data set, and
suggest the use of inhomogeneity-corrected data for future trend studies. This study is
supported by the European Union’s FP7 EartH2Observe "Global Earth Observation for
Integrated Water Resource Assessment" project (grant agreement number 331 603608). |
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