Based on the data of 33 sample trees from 15 permanent plots in Cunninghamia lanceolataplantations, the predicting models of crown average-surface area, crown max-surface area, crown average-volume, crown max-volume were discussed and were studied and set up. The results show that these four crown composite indexes had signiifcant relationships with other four tree variables, such as diameter at breast height, tree height, crown length, and crown width, the number increasing of tree variables can improve theiftting precision of the models, when the number increasing of tree variables were over two, theiftting precision of models cannot be obviously improved. The model test results show that the tree crown surface area model with crown length and tree height as the variables was the optimal; the crown volume model with crown width, crown length and tree height as the variables was the best; The estimated precisions of the optimal models were greater than 70.3%. Therefore, the models developed in this study are suitable for estimating the crown surface area and crown volume ofC.lanceolataplantations.%以福建杉木Cunninghamia lanceolata人工林为研究对象，基于15块样地的33株标准木的解析数据，探讨了树冠平均表面积、最大表面积、平均体积、最大体积4个复合指标的预测模型。结果表明：4个树冠复合指标与胸径、树高、冠长、冠幅之间呈显著相关，相应的增加变量可提高模型的拟合精度，但当模型变量增加到2个以上时，模型精度提高不大。模型检验结果表明，以冠幅和冠长为变量的树冠表面积模型为最优；树冠体积以冠幅、冠长和树高为变量的模型为最优。模型的预估精度P均大于70.3%，说明模型可以较好地预估杉木人工林的树冠表面积和体积。
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