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Abstract. Combining information derived from satellitebased passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m 3 m −3 ) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 ("transitional regions"), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied Correspondence to: Y. Y. Liu (yi.y.liu@csiro.au) to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

Abstract. 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).Correspondence to: W. A. Dorigo (wd@ipf.tuwien.ac.at)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 radiometerbased soil moisture datasets, e.g. for improved flood forecast modelling or the generation of superior multi-mission longterm soil moisture datasets.

The hydrological cycle is expected to intensify in response to global warming. Yet, little unequivocal evidence of such an acceleration has been found on a global scale. This holds in particular for terrestrial evaporation, the crucial return flow of water from land to atmosphere. Here we use satellite observations to reveal that continental evaporation has increased in northern latitudes, at rates consistent with expectations derived from temperature trends. However, at the global scale, the dynamics of the El Niño/Southern Oscillation (ENSO) have dominated the multi-decadal variability. During El Niño, limitations in terrestrial moisture supply result in vegetation water stress and reduced evaporation in eastern and central Australia, southern Africa and eastern South America. The opposite situation occurs during La Niña. Our results suggest that recent multi-year declines in global average continental evaporation8,9 reflect transitions to El Niño conditions, and are not the consequence of a persistent reorganization of the terrestrial water cycle. Future changes in continental evaporation will be determined by the response of ENSO to changes in global radiative forcing, which still remains highly uncertain

[1] Global trends in a new multi-satellite surface soil moisture dataset were analyzed for the period 1988-2010. 27% of the area covered by the dataset showed significant trends (p = 0.05). Of these, 73% were negative and 27% positive. Subtle drying trends were found in the Southern US, central South America, central Eurasia, northern Africa and the Middle East, Mongolia and northeast China, northern Siberia, and Western Australia. The strongest wetting trends were found in southern Africa and the subarctic region. Intra-annual analysis revealed that most trends are not uniform among seasons. The most prominent trend patterns in remotely sensed surface soil moisture were also found in GLDAS-Noah and ERA Interim modeled surface soil moisture and GPCP precipitation, lending confidence to the obtained results. The relationship with trends in GIMMS-NDVI appeared more complex. In areas of mutual disagreement more research is needed to identify potential deficiencies in models and/or remotely sensed products.

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