Estimating root mean square errors in remotely sensed soil moisture over continental scale domains

Root Mean Square Errors (RMSEs) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC) and error propagation through the soil moisture retrieval models (RMSEEP). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the local time series standard deviation (fRMSE). For both sensors, the fRMSE(TC) and fRMSE(EP) show similar spatial patterns of relatively high/low errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSE(TC) shows that in general both data sets have good skill over low to moderate vegetation cover. Additionally, they have similar accuracy even when considered by land cover class, although the AMSR-E fRMSEs show a stronger signal of the vegetation cover. (c) 2013 The Authors. Published by Elsevier Inc. All rights reserved.