在土壤湿度模拟研究领域，现有的数据同化方法均存在一定缺陷，为此，基于集合卡尔曼滤波( EnKF) 方法 和无迹变换，提出了无迹加权集合卡尔曼滤波( UWEnKF) ，增加了对结果影响较大的集合成员的权重，且集合成员关于 均值对称。结合一维 Ｒichards 方程在梅林流域开展了同化表层土壤湿度的实时同化实验，验证了其有效性。结果表明: ①一维 Ｒichards 方程可以较好地模拟出土壤湿度的动态变化过程; ②在土壤湿度模拟过程中引入数据同化方法，可以提 高土壤湿度的模拟精度，但同化效果与模拟精度有关; ③UWEnKF 同化方法能够显著改善土壤湿度的模拟结果，比 EnKF 同化方法效果更佳。总之，UWEnKF 是一种有效的、切实可行的数据同化方法，是提高土壤湿度模拟精度的有效手段。 

As the drawback exists in data assimilation method for soil moisture simulation，the unscented weighted ensemble Kalman filter ( UWEnKF) based on unscented transformation and ensemble Kalman filter ( EnKF) is proposed，which improves the weight of the important ensemble member，and the ensemble members are symmetry about the mean values． The performance of UWEnKF is verified through the soil moisture assimilation experiment by assimilating the hourly in situ soil surface moisture observations into the one － dimensional ( 1 － D) Ｒichard equation at Meilin study area，eastern China． The results show that ① the one－dimensional Ｒichards equation can well simulate the soil moisture change，② data assimilation method can improve the soil moisture simulation accuracy，but the performance of assimilation method is related to simulation accuracy，③ UWEnKF can significantly improve soil moisture simulations and perform better than EnKF obviously in improving the accuracy of the model． Overall，UWEnKF is an efficient and practical data assimilation method，and is an important technique to improve the soil moisture simulation accuracy．