An Improved Triple Collocation Analysis Algorithm for Decomposing Autocorrelated and White Soil Moisture Retrieval Errors

Abstract If not properly account for, autocorrelated retrieval errors can lead to inaccurate results in soil moisture data analysis and reanalysis. Here we propose a more generalized form of the triple collocation analysis algorithm (GTC) capable of decomposing the total error variance of remotely sensed surface soil moisture retrievals into their autocorrelated and the serially white components. Synthetic tests demonstrate the robustness and accuracy of GTC—even in the presence of significant temporal data gaps. However, the accuracy of GTC error autoregressive parameter estimates is relatively more sensitive to temporal data availability. In addition, land surface model soil moisture predictions collected from phase 2 of the North American Land Data Assimilation System and remotely sensed surface soil moisture retrievals obtained from the European Space Agency Climate Change Initiative (ESA CCI) are applied for a real data demonstration. Despite expectations to the contrary, significant error autocorrelation is found in the remotely sensed‐based ESA CCI soil moisture data sets. In particular, ESA CCI‐Act (i.e., the subset of ESA CCI soil moisture retrievals based on active scattomotter data) demonstrates the largest autoregressive parameters over low biomass areas. Conversely, ESA CCI‐Pas retrievals (based on passive radiometer data) have larger error autoregressive parameters over high biomass areas. As such, results clarify circumstances in which errors in remotely sensed surface soil moisture retrievals cannot be considered serially white.