Talks and Poster Presentations (without Proceedings-Entry):
A. Gruber, W. Dorigo, S. Hahn, A. Xaver, W. Wagner, L. Brocca:
"Global characterization of SMOS level 2 soil moisture using various evaluation methods";
Talk: SMOS Science Workshop,
The importance of soil moisture has been underlined by the Global Climate Observing System GCOS by endorsing it as an Essential Climate Variable (ECV). Among several past and existing missions observing soil moisture from space the Soil Moisture and Ocean Salinity Mission (SMOS) with its innovative 2D interferometric radiometer observing in L-band is expected to play a key role in the generation of a soil moisture climate data record. Nevertheless, the various satellite based sensors used to retrieve soil moisture highly differ in the measurement principle (active or passive) as well as in the used frequency range and used signal polarizations, which results in differences in the depth of the observed layer, the influence of vegetation and RFI and, more general, the characteristics of the soil moisture retrievals. Therefore, in order to merge the different datasets into a single soil moisture climate data record, an in-depth understanding of the quality of the different sensors is indispensable. Several approaches can be used to determine the quality of the products. Inter-comparison between the satellites or with in-situ measurements mainly describes the dynamics of the measurements and the sensitivity to precipitation events, comparison with model data can be used to show drifts and systematic biases, and the triple collocation method gives information about the random noise of the retrievals. All these approaches are important to provide a comprehensive quality description of soil moisture observations, but the differences in the error characteristics of the sensors, the varying spatial and temporal resolution of the products, the lack of global spread high-density in-situ measurement sites, and other facts make the choice of a single validation technique together with the optimum combination of datasets very critical. In this study we will bring different methods and datasets from active and passive soil moisture missions (AMSR-E, ASCAT), land surface models (GLDAS-Noah) and the in-situ stations of the International Soil Moisture Network (ISMN) together to characterize the performance of SMOS over various biogeophysical conditions worldwide.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.