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Soil moisture is one of the essential climate variables for the Global Climate Observing System (GCOS) that has been prioritized by the ESA's Climate Change Initiative to construct its homogeneous long-term climate record. This requires a consistent characterization of the error structures in the individual data sets, which vary due to changes in instrument configuration and calibration, and retrieval algorithm design. In this paper, the random error and systematic differences in nine passive and active microwave satellite soil moisture products over Australia (time coverage: 1978–present) are estimated in a same manner for SM components at subseasonal and seasonal-to-interannual timescales separately. The multi-scale error structures are found to be non-trivial and vary between the products, giving cause for conducting multi-scale merging with awareness of these differences. Noticeable similarities between the error structures of the satellite products derived from same retrieval algorithm and same measuring frequency however suggest transferability of error parameters between them. Using partial rank correlation analysis, the error maps are linked to statistics on vegetation index, digital elevation, soil moisture and soil temperature, and land cover fractions and mixing in order to explain the observed variability and the similarities between the products. [ABSTRACT FROM AUTHOR]

Abstract: The use of satellite-based soil moisture retrievals for hydrologic, meteorological and climatological applications is advancing significantly due to increasing capability and temporal coverage of current and future missions. Characterisation of the relative skill of soil moisture products from different satellite sensors on a common spatial grid is crucial to achieve synergetic applications. This paper therefore evaluates three soil moisture products from AMSR-E (Advanced Microwave Scanning Radiometer — Earth Observing System), ASCAT (Advanced Scatterometer) and SMOS (Soil Moisture and Ocean Salinity) in absolute soil moisture units and on a common grid, against in-situ observations from southeast Australia. Before renormalisation, the three products yield correlations of 0.63–0.71 and a similar root-mean-square difference (RMSD) in the order of 0.1m3 m−3, although showing different levels of error contributions from bias, variance and correlations. The results are compared with land and precipitation data to investigate the sensitivity of their errors to land surface features. Three renormalisation strategies – minimum–maximum matching, mean/standard-deviation (μ–σ) matching and cumulative distribution function (CDF) matching – are considered for correcting systematic differences between ground and satellite data. The renormalised satellite data is found to retain RMSDs of 0.04–0.06m3 m−3 on average. The CDF method produces only marginal further improvements to correlations (0.67–0.75) and RMSDs compared to the μ–σ approach. The renormalisations by μ–σ and CDF methods also bring three products into better agreements with each other, but lead to strong correlations between RMSD and the dynamic range of in-situ soil moisture. [Copyright &y& Elsevier]