ALBERT

Description: Global soil moisture records are essential for studying the role of hydrologic processes within the larger earth system. Various studies have shown the benefit of assimilating satellite-based soil moisture data into water balance models or merging multi-source soil moisture retrievals into a unified data set. However, this requires an appropriate parameterization of the error structures of the underlying data sets. While triple collocation (TC) analysis has been widely recognized as a powerful tool for estimating random error variances of coarse-resolution soil moisture data sets, the estimation of error cross-covariances remains an unresolved challenge. Here we propose a method - referred to as extended collocation (EC) analysis - for estimating error cross-correlations by generalizing the TC method to an arbitrary number of data sets and relaxing the therein made assumption of zero error cross-correlation for certain data set combinations. A synthetic experiment shows that EC analysis is able to reliably recover true error cross-correlation levels. Applied to real soil moisture retrievals from AMSR-E C-band and X-band observations together with ASCAT retrievals, modelled data from GLDAS-Noah, and in situ measurements drawn from the International Soil Moisture Network, EC yields reasonable and strong non-zero error cross-correlations between the two AMSR-E products. Against expectation, non-zero error cross-correlations are also found between ASCAT and AMSR-E. We conclude that the proposed EC method represents an important step towards a fully parameterized error covariance matrix for coarse-resolution soil moisture data sets, which is vital for any rigorous data assimilation framework or data merging scheme.