Impact of Bias Correction Methods on Estimation of Soil Moisture When Assimilating Active and Passive Microwave Observations

Abstract

In this paper, bias correction approaches are investigated to understand their impact on assimilating active and/or passive microwave observations on near-surface soil moisture (SM) estimates. Synthetic and field observations were assimilated in a soil–vegetation–atmosphere transfer model linked with an integrated active–passive model at L-band for bare soil. The two bias correction methods included in this study are the online bias correction with feedback (BCWF) with extended implementation with nonlinear observation operators and the simultaneous state parameter (SSP) update. New equations for BCWF were derived for the case of nonlinear observation operators because current versions of this approach were not applicable for improving SM by assimilating microwave observations. In SSP, the bias is compensated by tunning the values of the parameters. The two approaches resulted in similar accuracy for improving SM estimates compared with the uncorrected estimates. SSP showed the highest certainty for both synthetic and field observations. Using the bias correction methods, the mean estimates of SM improved by up to 88%, 87%, and 94%, when passive, active, and active–passive synthetic observations were assimilated, respectively, compared with the open-loop estimates. In contrast, when assimilating field observations from the Eleventh Microwave Water Energy Balance Experiment, the mean estimates of SM improved by up to 44%, 18%, and 48%, when passive, active, and active–passive observations were assimilated, respectively, compared with open-loop estimates. The decrement in improving the SM estimates suggests sources of uncertainty other than those from model parameters and forcings.