Leaves are considered a significant site of carbon-water exchange and the energy-balance process. The characteristics of leaf response to the external environment, functional traits, coordination mechanisms, and trade-off strategies, as well as the structure and variation of leaves, have recently attracted huge interest. Further, leaf size could directly affect their capacity for light interception and carbon acquisition. Leaf area (LA) and related leaf traits such as specific leaf area (SLA), leaf area index (LAI), and normalized difference vegetation index (NDVI) are the key indicators in crop breeding, agroforestry production and management, model estimation, and species structure variation and functional adaptation mechanisms. The leaf traits are based on leaf area and influenced by leaf morphology and size, and determination of leaf area is the basis to discuss plant photosynthetic production and the physiological-ecological process. However, the uncertainty of needle leaf area, which is due to the difficulty of measurement, could be a hindrance to efficient production and management, effective risk assessment, and development of correlational research. Thus, it is significant to explore the appropriate measurement methods to obtain accurate values of leaf area. There is lack of comparative studies between different methods of measuring leaf area, resulting in inconsistency of associated concepts and definitions. Presently, instrument-based measurement methods of LA are pervasive and prevalent, but lack calibration by artificial measurements. Additionally, studies with regression estimation models based on plant leaf area and morphometric characteristics are concentrated on agronomy products such as crops and fruit and some broadleaf species. Research that centers on needle leaf area estimation models based on leaf morphological characteristics is still lacking. This study used Cunninghamia lanceolata, a common pioneer tree species in southern China, to measure 3 leaf morphometric characteristics (leaf length, leaf maximum width, leaf maximum thickness) and indirectly calculate 5 indicators (leaf mean width, leaf mean thickness, leaf area, leaf elongation, leaf perimeter) using Vernier calipers and portable leaf area meter. We present the statistical distribution and correlation analysis of the 8 morphological characteristics, fitting the leaf area with the 7 other indicators in multivariate linear regression models and nonlinear regression index models. We find that (1) by manual measurement, the credible simple leaf area of Chinese fir ranges from 0.758 cm² to 0.836 cm², and shows a maximum coefficient of variation (CV=0.513); (2) leaf area significantly correlates with leaf length and width (r=0.896, 0.682); (3) the multivariate linear regression model of leaf length and width that is most accurate:Y=-0.388 + 0.165X₁-0.023X₂ + 1.453X₃ (R² =0.981, SE=0.053), where X₁, X₂, and X₃ are leaf perimeter, leaf elongation, and leaf mean width, respectively. From the point of view of simplicity, leaf length (LL) of a single variable index model is more suited for a leaf area estimation model:LA=0.1×(1 + LL) ^1.398 (R² =0.77), χ² =0.39. The results demonstrated that this is a method of instrument calibration and accurate estimation of other leaf traits of Chinese fir, providing data about single leaf area and improving the model accuracy and stability for Chinese fir leaf area estimation. Moreover, this approach is effective in providing data to support advice to plantation management, and for verification and improvement of leaf morphology and leaf functional traits of other species.