000187158 001__ 187158 000187158 005__ 20220930130038.0 000187158 0247_ $$2doi$$a10.1016/j.jhydrol.2014.12.038 000187158 0247_ $$2ISSN$$a0022-1694 000187158 0247_ $$2ISSN$$a1879-2707 000187158 0247_ $$2WOS$$aWOS:000350920200016 000187158 037__ $$aFZJ-2015-00832 000187158 041__ $$aEnglish 000187158 082__ $$a690 000187158 1001_ $$0P:(DE-Juel1)144811$$aRötzer, K.$$b0$$eCorresponding Author$$ufzj 000187158 245__ $$aSpatio-temporal variability of global soil moisture products 000187158 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2015 000187158 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1422358327_24687 000187158 3367_ $$2DataCite$$aOutput Types/Journal article 000187158 3367_ $$00$$2EndNote$$aJournal Article 000187158 3367_ $$2BibTeX$$aARTICLE 000187158 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000187158 3367_ $$2DRIVER$$aarticle 000187158 520__ $$aBeing an important variable for various applications, for example hydrological and weather prediction models or data assimilation, a large range of global soil moisture products from different sources, such as modeling or active and passive microwave remote sensing, are available. The diverse measurement and estimation methods can lead to differences in the characteristics of the products. This study investigates the spatial and temporal behavior of three different products: (i) the Soil Moisture and Ocean Salinity (SMOS) Level 2 product, retrieved with a physically based approach from passive microwave remote sensing brightness temperatures, (ii) the MetOp-A Advanced Scatterometer (ASCAT) product retrieved with a change detection method from radar remote sensing backscattering coefficients, and (iii) the ERA Interim product from a weather forecast model reanalysis. Results show overall similar patterns of spatial soil moisture, but high deviations in absolute values. A ranking of mean relative differences demonstrates that ASCAT and ERA Interim products show most similar spatial soil moisture patterns, while ERA and SMOS products show least similarities. For selected regions in different climate classes, time series of the ASCAT product generally show higher variability of soil moisture than SMOS, and especially than ERA products. The relationship of spatial mean and variance is, especially during wet periods, influenced by sensor and retrieval characteristics in the SMOS product, while it is determined to a larger degree by the precipitation patterns of the respective regions in the ASCAT and ERA products. The decomposition of spatial variance into temporal variant and invariant components exhibits high dependence on the retrieval methods of the respective products. The change detection retrieval method causes higher influence of temporal variant factors (e.g. precipitation, evaporation) on the ASCAT product, while SMOS and ERA products are stronger determined by temporal invariant factors (e.g. topography, soil characteristics). The investigation of the effect of changing scales on spatial variance in three different areas indicates that the variance does not vary with increasing support scale. Increasing extent scales from 250 to 3000 km raise spatial variance of all products and all study areas according to a power law, which is varying seasonally. ERA shows a consistent scaling behavior with a constant power scale factor and similar intercepts across all study regions. In general, the investigated products show overall different spatial and temporal statistics which are induced by their different estimation methods and which are important to be aware of for the selection of a product for application and for their up- or downscaling. 000187158 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x0 000187158 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x1 000187158 588__ $$aDataset connected to CrossRef, juser.fz-juelich.de 000187158 7001_ $$0P:(DE-Juel1)129506$$aMontzka, C.$$b1$$ufzj 000187158 7001_ $$0P:(DE-Juel1)129549$$aVereecken, H.$$b2$$ufzj 000187158 773__ $$0PERI:(DE-600)1473173-3$$a10.1016/j.jhydrol.2014.12.038$$gVol. 522, p. 187 - 202$$p187 - 202$$tJournal of hydrology$$v522$$x0022-1694$$y2015 000187158 8564_ $$uhttps://juser.fz-juelich.de/record/187158/files/FZJ-2015-00832.pdf$$yRestricted 000187158 8767_ $$92015-01-08$$d2015-02-03$$eColour charges$$jZahlung erfolgt 000187158 909CO $$ooai:juser.fz-juelich.de:187158$$pVDB:Earth_Environment$$pVDB$$pOpenAPC$$popenCost 000187158 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR 000187158 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index 000187158 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded 000187158 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection 000187158 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List 000187158 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS 000187158 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline 000187158 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz 000187158 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews 000187158 915__ $$0StatID:(DE-HGF)1060$$2StatID$$aDBCoverage$$bCurrent Contents - Agriculture, Biology and Environmental Sciences 000187158 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology 000187158 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5 000187158 9141_ $$y2015 000187158 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144811$$aForschungszentrum Jülich GmbH$$b0$$kFZJ 000187158 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129506$$aForschungszentrum Jülich GmbH$$b1$$kFZJ 000187158 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129549$$aForschungszentrum Jülich GmbH$$b2$$kFZJ 000187158 9130_ $$0G:(DE-HGF)POF2-246$$1G:(DE-HGF)POF2-240$$2G:(DE-HGF)POF2-200$$aDE-HGF$$bErde und Umwelt$$lTerrestrische Umwelt$$vModelling and Monitoring Terrestrial Systems: Methods and Technologies$$x0 000187158 9130_ $$0G:(DE-HGF)POF3-255$$1G:(DE-HGF)POF3-250$$2G:(DE-HGF)POF3-200$$aDE-HGF$$bMarine, Küsten- und Polare Systeme$$lTerrestrische Umwelt$$vTerrestrial Systems: From Observation to Prediction$$x1 000187158 9131_ $$0G:(DE-HGF)POF3-255$$1G:(DE-HGF)POF3-250$$2G:(DE-HGF)POF3-200$$aDE-HGF$$bMarine, Küsten- und Polare Systeme$$lTerrestrische Umwelt$$vTerrestrial Systems: From Observation to Prediction$$x0 000187158 920__ $$lyes 000187158 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0 000187158 980__ $$ajournal 000187158 980__ $$aVDB 000187158 980__ $$aI:(DE-Juel1)IBG-3-20101118 000187158 980__ $$aUNRESTRICTED 000187158 980__ $$aAPC