Soil moisture estimation using an improved particle filter assimilation algorithm

Soil moisture is one of the key environmental variables in the Earth science. Data assimilation (DA) provides a way to effectively combine model simulations and observations, thus can yield superior soil moisture estimations. Among various DA methods, the particle filter (PF) is free from the constraints of linear models and Gaussian error distributions, thus receiving increasing attention in DA. However, the particle degeneracy still remains a major problem in practical application of PF. In this paper, an improved PF is proposed based on ensemble Kalman filter (EnKF) and the Markov Chain Monte Carlo (MCMC) method. The improved PF is tested by assimilating brightness temperatures from the Advanced Microwave Scanning Radiometer (AMSR-E) into the variance infiltration capacity (VIC) model to estimate soil moisture in the NaQu network region at the Tibetan Plateau. The experiment results show that the improved PF can provide more accurate soil moisture estimations than the EnKF and standard PF, thus demonstrating the effectiveness of the improved PF.