An improved triple collocation algorithm for decomposing autocorrelated and serially-white soil moisture retrieval errors

If not properly account for, auto-correlated 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 algorithm (GTC) capable of decomposing the total error variance of remotely-sensed surface soil moisture retrievals into their autocorrelated and the serially-white temporal components. Synthetic tests demonstrate the robustness and accuracy of the GTC algorithm - 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 (NLDAS-2) 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 datasets. In particular, ESA CCI-Act (i.e., the subset of ESA CCI SM retrievals based on active scattomotter data) demonstrates the largest autoregressive parameters over low vegetation biomass areas. Conversely, ESA CCI-Pas retrievals (based on passive radiometer data) has 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.