Leaf chlorophyll content (LCC) is an indicator of leaf photosynthetic capacity. It is crucial for improving the understanding of plant physiological status. SPAD meters are routinely used to provide an instantaneous estimation of in situ LCC. However, the calibration of meter readings into absolute measures of LCC is difficult, and a generic approach for this conversion remains elusive. This study presents an evaluation of the approaches that are commonly used in converting SPAD readings into absolute LCC values. We compared these approaches using three field datasets and one synthetic dataset. The field datasets consist of LCC measured using a destructive method in the laboratory, as well as the SPAD readings measured in the field for various vegetation types. The synthetic dataset was generated with the leaf radiative transfer model PROSPECT-5 across different leaf structures. LCC covers a wide range from 1.40 μg cm⁻² to 86.34 μg cm⁻² in the field datasets, and it ranges from 5 μg cm⁻² to 80 μg cm⁻² in the synthetic dataset. The relationships between LCC and SPAD readings were examined using linear, polynomial, exponential, and homographic functions for the field and synthetic datasets. For the field datasets, the assessments of these approaches were conducted for (i) all three datasets together, (ii) individual datasets, and (iii) individual vegetation species. For the synthetic dataset, leaves with different leaf structures (which mimic different vegetation species) were grouped for the evaluation of the approaches. The results demonstrate that the linear function is the most accurate one for the simulated dataset, in which leaf structure is relatively simple due to the turbid medium assumption of the PROSPECT-5 model. The assumption of leaves in the PROSPECT-5 model complies with the assumption made in the designed algorithm of the SPAD meter. As a result, the linear relationship between LCC and SPAD values was found for the modeled dataset in which the leaf structure is simple. For the field dataset, the functions do not perform well for all datasets together, while they improve significantly for individual datasets or species. The overall performance of the linear ($\mathrm{LCC}=a\ast \mathrm{SPAD}+b$), polynomial ($\mathrm{LCC}=a\ast {\mathrm{SPAD}}^2+b\ast \mathrm{SPAD}+c$), and exponential functions ($\mathrm{LCC}=0.0893\ast \left({10}^{{\mathrm{SPAD}}^{\alpha }}\right))$ is promising for various datasets and species with the R² > 0.8 and RMSE <10 μg cm⁻². However, the accuracy of the homographic functions ($\mathrm{LCC}=a\ast \mathrm{SPAD}/\left(b-\mathrm{SPAD}\right)$) changes significantly among different datasets and species with R² from 0.02 of wheat to 0.92 of linseed (RMSE from 642.50 μg cm⁻² to 5.74 μg cm⁻²). Other than species- and dataset-dependence, the homographic functions are more likely to produce a numerical singularity due to the characteristics of the function per se. Compared with the linear and exponential functions, the polynomial functions have a higher degree of freedom due to one extra fitting parameter. For a smaller size of data, the linear and exponential functions are more suitable than the polynomial functions due to the less fitting parameters. This study compares different approaches and addresses the uncertainty in the conversion from SPAD readings into absolute LCC, which facilitates more accurate measurements of absolute LCC in the field.