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 scatterometers onboard the European Remote Sensing satellites (ERS-1 & ERS-2) and the METeorological OPerational satellite (METOP) have been shown to be useful for surface soil moisture retrieval using the so-called TU-Wien change detection method. This paper presents an improved soil moisture retrieval algorithm based on the existing TU-Wien method but with new parameterization as well as a series of modifications. The new algorithm, WAter Retrieval Package 5 (WARP5), copes with some limitations identified in the earlier method WARP4 and provides the possibility of migrating soil moisture retrieval from ERS-SCAT to METOP-ASCAT data. The WARP5 algorithm results in a more robust and spatially uniform soil moisture product, thanks to its new processing elements, including a method for the correction of azimuthal anisotropy of backscatter, a comprehensive noise model, and new techniques for calculation of the model parameters. Cross-comparisons of WARP4 and WARP5 data sets with the Oklahoma Mesonet in situ observations and also with European Centre of Medium Range Weather Forcast (ECMWF) ReAnalysis (ERA-Interim) global modeled data show that the new algorithm has a better performance and effectively corrects retrieval errors in certain areas.

[1] This article presents first results of deriving relative surface soil moisture from the METOP-A Advanced Scatterometer. Retrieval is based on a change detection approach which has originally been developed for the Active Microwave Instrument flown onboard the European satellites ERS-1 and ERS-2. Using model parameters derived from eight years of ERS scatterometer data, first global soil moisture maps have been produced from ASCAT data. The ASCAT data were distributed by EUMETSAT for validation purposes during the ASCAT product commissioning activities. Several recent cases of drought and excessive rainfall are clearly visible in the soil moisture data. The results confirm that seamless soil moisture time series can be expected from the series of two ERS and three METOP scatterometers, providing global coverage on decadal time scales (from 1991 to about 2021). Thereby, operational, nearreal-time ASCAT soil moisture products will become available for weather prediction and hydrometeorological applications.

In the last few years, research made significant progress towards operational soil moisture remote sensing which lead to the availability of several global data sets. For an optimal use of these data, an accurate estimation of the error structure is an important condition. To solve for the validation problem we introduce the triple collocation error estimation technique. The triple collocation technique is a powerful tool to estimate the root mean square error while simultaneously solving for systematic differences in the climatologies of a set of three independent data sources. We evaluate the method by applying it to a passive microwave (TRMM radiometer) derived, an active microwave (ERS‐2 scatterometer) derived and a modeled (ERA‐Interim reanalysis) soil moisture data sets. The results suggest that the method provides realistic error estimates.