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Land cover area estimation is one of the obvious applications of remote sensing. This paper compared two categories of area estimators, the confusion matrix calibration and the regression estimators, under various sample sizes and classification accuracy. Based on the relationship between the error distribution and heterogeneity of remote sensing data, simulation procedure was conducted to acquire the specific and controllable accuracy classification and preserve a real land cover structure pattern. And two criteria, the average absolute relative bias and CV, were adopted to evaluate the performance of estimators. The results suggest that (1) these estimators are asymptotically unbiased and zero-dispersed as sample size and the classification accuracy increases; (2) the high-quality classification data play a positive role in estimation and it is more meaningful for the confusion matrix estimators; (3) the regression estimators are slightly superior to the confusion matrix calibration estimators under acceptable classification accuracy level based on two criteria adopted.