提出基于集合卡尔曼滤波(EnKF)的元胞自动机(CA)模型。在CA模型中，由于不同的样本会训练出不同参数值 的转换规则，且获取的转换规则在整个模拟过程中不能改变等原因，误差在模拟过程中会不断累积。本文在CA模型中 引入集合卡尔曼滤波的数据同化方法，建立了基于集合卡尔曼滤波的数据同化CA模型，同化遥感观测数据，根据得出 的同化值修正模拟结果使之向真实情况逼近。利用该模型模拟了广东省东莞市的发展情景(1995年—2005年)，实验表 明，与传统CA模型相比，基于集合卡尔曼滤波的CA模型能够融合遥感观测数据，并能更有效地模拟城市扩张过程，达 到良好的模拟效果。

This paper presents a new method for calibrating urban cellular automata (CA) using ensemble Kalman fi lter (EnKF) of data assimilation. In CA modeling, the key issue is defi ning transition rules, which usually consist of many variables and parameters. There are many uncertainties in determining parameter values and the transition rules are deterministic and unchanged during the modeling process. As a result, the model errors would accumulate continuously in the simulation. The paper introduces the ensemble Kalman fi lter of data assimilation into CA model, and a data assimilation CA model based on ensemble Kalman fi lter was established. After using the model, the paper can derive analyzed values by merging information from remotely sensed observations with CA model predictions, and modify the simulated results closer to actual situation based on the analysis values. The proposed model has been tested in Dongguan, a city in the Pearl River Delta of Southern China. Experiments indicate that the method can reduce the model error in the simulation and help to generate more reliable simulation results by comparing variance, accuracy and kappa coeffi cient.