Soil organic carbon (SOC) plays an important role in controlling the function and quality of soil and offsetting the emissions of greenhouse gases. However, the dynamic monitoring and estimation of SOC are very difficult due to the complex traditional methods and the changing environmental variables. For instance, the calculation of SOC stock requires measurement of a few relevant soil attributes, such as soil organic matter (SOM), soil bulk density (SBD), soil moisture, and soil weight, in the laboratory. Many studies have suggested that visible and near-infrared (vis–NIR) spectra are a practical and affordable alternative to accurately and rapidly estimate the soil attributes relevant to SOC stock, and airborne hyperspectral images can be used as a valuable data source to perform digital soil mapping with high spatial resolution. The objective of this research was to check the predicted capability of SOC stock through laboratory and airborne vis–NIR spectral data. A total of 50 topsoil samples (0–15 cm) from the farmland of Iowa City were used as the study object. The partial least squares regression model was used to predict SOC stock through the direct and indirect methods. In the direct method, the SOC stock was predicted using the spectral data. In the indirect method, the relevant soil properties (SOM and SBD) of the SOC stock were predicted using the spectral data, and then the SOC stock was calculated. The mechanism of the prediction methods and the potential influencing factors of the model performance were discussed from the aspect of electromagnetic theory and empirical statistics. Results showed the following: (i) SOC stock can be successfully predicted using the laboratory spectra and the airborne hyperspectral image through the direct and indirect methods; (ii) the SOC stock and its relevant soil properties (SOM and SBD) showed evident spectral absorption characteristics in the vis–NIR spectral band; (iii) the atmospheric environment and soil surface conditions were the main influencing factors of the prediction accuracy between the airborne and laboratory spectra. This research might be useful for the dynamic monitoring and modeling of SOC in agricultural and environmental fields.