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R. De Jeu, W. Dorigo, W. Wagner, D. Chung, R. Kidd, R. Parinussa, Y. Liu, E. Haas, M. Ertl, W. Lahoz, S. Seneviratne, H. Mistelbach, M. Hirschi, Ned Dwyer, C. Pratula, K. Rautiainen, P. Lecomte:
"The development of a 30 +year climate soil moisture record from passive and active microwave satellite observations";
Talk: American Geophysical Union (AGU) Fall Meeting 2012, San Francisco, CA, USA; 2012-12-03 - 2012-12-07.

Since the launch of one of the first Earth Observation platforms in the seventies, i.e. Nimbus-7, a long legacy of satellites suitable for global soil moisture monitoring was built up. Several research groups have used these sensors to derive soil moisture datasets. These datasets vary in quality but also have several things in common: they all provide global soil moisture estimates of the first top centimeters at a rather coarse spatial resolution (25-50 km). These global datasets increasingly prove their value in environmental research, however, from a climate perspective these different datasets become even more valuable if we are able to combine these different datasets into one consistent multi-decadal data record. This scientific task was first addressed as part of the Water Cycle Multimission Observation Strategy (WACMOS) project from the Support To Science Element (STSE) program of the European Space Agency (ESA) and later became a key research goal within the ESA Climate Change Initiative soil moisture project. As a first step two extensively validated soil moisture products were selected to create a harmonized dataset; one from the Vienna University of Technology (TU Wien) based on active microwave observations and one from the VU University Amsterdam in collaboration with NASA based on passive microwave observations. The harmonization of these datasets incorporates the advantage of both microwave techniques and spans the entire period from 1978 onwards. A statistical methodology based on scaling, ranking, and blending was developed to address these issues to create one consistent dataset. A soil moisture dataset provided by a land surface model (GLDAS-1-Noah) was used to scale the different satellite-based products to the same range. The blending of the active and passive data sets was based on their respective sensitivity to vegetation cover. While this approach imposes the absolute values of the land surface model dataset to the final product, it preserves the relative dynamics (e.g., seasonality, inter-annual variations) and trends of the original satellite derived retrievals. The climate data record is currently being validated with in-situ soil moisture observations and cross-checked with other variables playing a major role within the hydrological cycle, such as precipitation and evaporation. In addition, indirect proxies like tree ring width data are used to study the consistency of the 30 +year dataset. Finally, this method allows the long term product to be extended with data from other current and future operational satellites and will be further improved as part of ESA Climate Change Initiative program.