The land data assimilation research has become the emerging domain in the geoscience, the data assimilation algorithm obtained rapid development and widespread application taking the nonlinear filter as representative's. The extended Kalman filter, unscented Kalman filter, the ensemble Kalman filter and the SIR particle filter are discussed in the Bayesian theory framework from the viewpoint of the recursive Bayesian estimation. Towards the problems in the application of the ensemble Kalman filter and the SIR particle filter, some techniques that can improve the filter performances are also reviewed, such as the covariance localization, the covariance inflation, the double ensemble Kalman filter, the perturbations in the ensembles, the model forcing and parameters, the ensemble square root Kalman filter and the improved variants of the particle filters. The advantages and disadvantages of each filter as well as the applied perspective and the future research directions are discussed.