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Error characterisation of satellite-retrieved soil moisture (SM) is crucial for maximizing their utility in research and applications in hydro-meteorology and climatology. It can provide insights for retrieval development and validation, and inform suitable strategies for data fusion and assimilation. Su et al. (2013a) proposed a potential Fourier method for quantifying the errors based on the difference between the empirical power spectra of these SM data and a water balance model via spectral fitting (SF), circumventing the need for any ancillary data. This work first evaluates its utility by estimating the errors in two passive and active microwave satellite SM over Australia, and comparing the results against the triple collocation (TC) estimator. The SF estimator shows very good agreement with TC in terms of error standard deviation and signal-to-noise ratio, with strong linear correlations of 0.80–0.92 but with lower error estimates. As the two estimators are not strictly comparable, their strong agreement suggests a strong complementarity between time-domain and frequency-domain analyses of errors. A better understanding of the spectral characteristics of the error is still needed to understand their differences. Next, spatial analyses of the derived (SF and TC) error maps, in terms of error standard deviation and noise-to-signal ratio, for the two satellite data are performed with principal component analysis to identify influence of vegetation/leaf-area index (LAI), rainfall, soil wetness, and spatial heterogeneity in topography and soil type on retrieval errors. Lastly, seasonal analysis of the errors discovers systematic temporal variability in errors due to variability in rainfall amount, and less so with changing LAI. [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]

The Advanced Microwave Scanning Radiometer 2 (AMSR2), a follow-up microwave sensor to the AMSR for Earth Observing System (AMSR-E), was launched on the Global Change Observation Mission 1 – Water (GCOM-W1) satellite in May 2012. It is as yet unclear if instrumental improvements in AMSR2 over AMSR-E have led to better soil moisture (SM) estimates, especially since there is no overlapping period of data between the sensors. This study focuses on comparing the results of AMSR2 and AMSR-E SM over Australia, distinguishing four Köppen climate zones to determine if AMSR2 is better than AMSR-E. This is achieved by selecting two year-long comparative time periods from the operating periods of AMSR-E and AMSR2, based on their statistical similarities in modeled SM as a proxy, using Modern Era Retrospective-analysis for Research and Applications-Land (MERRA-L). The AMSR2 and AMSR-E C- and X-band SM derived from the Land Parameter Retrieval Model (LPRM) was evaluated. Both AMSR2 C- and X-band SM products were found to show similar temporal patterns and spatial agreement with AMSR-E C- and X-band SM, supported by unbiased root mean square difference (ubRMSD) and R-values with MERRA-L SM, respectively. Using lag-based instrumental variable analysis to estimate the random error component of SM retrievals, the noise-to-signal ratios in AMSR2 X-band SM were found to be slightly higher than their AMSR-E counterparts. The improvements in AMSR2, such as the superior radiometric sensitivity and spatial resolution, have therefore not led to statistically significant differences in performance for LPRM retrievals at 1/2° × 1/2° grid resolution, when compared with AMSR-E. However, similarities in the metrics for AMSR2 and AMSR-E SM suggest that AMSR2 provides a valuable continuation to AMSR-E. [ABSTRACT FROM AUTHOR]

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]