Simultaneous estimation of surface soil moisture and soil properties with a dual ensemble Kalman smoother

In this paper, a new state-parameter estimation approach is presented based on the dual ensemble Kalman smoother (DEnKS) and simple biosphere model (SiB2) to sequentially estimate both the soil properties and soil moisture profile by assimilating surface soil moisture observations. The Arou observation station, located in the upper reaches of the Heihe River in northwestern China, was selected to test the proposed method. Three numeric experiments were designed and performed to analyze the influence of uncertainties in model parameters, atmospheric forcing, and the model's physical mechanics on soil moisture estimates. Several assimilation schemes based on the ensemble Kalman filter (EnKF), ensemble Kalman smoother (EnKS), and dual EnKF (DEnKF) were also compared in this study. The results demonstrate that soil moisture and soil properties can be simultaneously estimated by state-parameter estimation methods, which can provide more accurate estimation of soil moisture than traditional filter methods such as EnKF and EnKS. The estimation accuracy of the model parameters decreased with increasing error sources. DEnKS outperformed DEnKF in estimating soil moisture in most cases, especially where few observations were available. This study demonstrates that the DEnKS approach is a useful and practical way to improve soil moisture estimation.