【Objective】A stand dominant height estimation model for Cunninghamia lanceolata (Chinese fir) plantations was developed to provide a foundation for site quality assessment and growth prediction of Chinese fir plantations.【Method】Based on survey data from Chinese fir plantation plots from the two phases of the National Forest and Grassland Ecosystem Integrated Monitoring Program in 2021 and 2022, we constructed dominant height estimation models using the ordinary least squares regression (OLS), the independent measurement error-in-variable model (IEIVM), and the simultaneous measurement error-in-variable equations (SEIVE). These models were used to realize the conversion among stand dominant height, stand mean height, and stand mean diameter at breast height (DBH), and the models were evaluated using indicators such as the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE).【Result】(1) The stand dominant height estimation models established by the IEIVM and SEIVE methods performed significantly better than that by the OLS method, with R² ranging from 0.697 to 0.718 and RRMSE is between 16.08% and 16.59%. (2) After considering the error structure among variables, the SEIVE method was slightly superior to the IEIVM method, and the fitting effect of the model was better, and it could realize accurate mutual predictions in the three variables.【Conclusion】The stand dominant height estimation model of Chinese fir based on SEIVE and considering the error structures among variables has good applicability and predictive performance. It supports the sequential prediction of mean DBH to mean height and dominant height, which provides a fundamental model for the site quality evaluation of Chinese fir plantations.