This paper presents a new method for calibrating urban cellular automata（CA） using ensemble Kalman filter（EnKF） of data assimilation.In CA modeling

the key issue is defining 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 filter of data assimilation into CA model

and a data assimilation CA model based on ensemble Kalman filter 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 coefficient.