Leaf area index (LAI) is one of the most important structural characteristics for understanding cotton (Gossypium hirsutum L.) growth, yield, and population structure. Destructive measurements are tedious, time consuming, and labor intensive. Modern techniques such as remote sensing and measurements from ground-based optical instruments are non-destructive and effective methods to rapidly measure LAI. The objective of this study was to determine the feasibility of using images from a common digital camera to measure LAI of cotton. We compared the results obtained using a digital camera with those obtained using a destructive sampling method and an LAI-2000 Plant Canopy Analyzer. Three field experiments were conducted with different planting densities, cultivars, nitrogen rates, and irrigation rates. A digital camera, an LAI-2000 Plant Canopy Analyzer, and an LI-191SA linear quantum sensor were used to observe the cotton canopy and record data. Leaves were also sampled destructively at their main growth stages. The digital camera images were captured looking downwards onto the canopy, and then an algorithm was used to separate the components of each image into four classes; sunlit leaves (SL), sunlit soil (SS), shaded leaves (ShL), and shaded soil (ShS). The parameter of image transmittance (T_imag) was calculated from SL and SS based on the Beer-Lambert Law. The validity of T_imag was analyzed and a quantitative model of T_imag and LAI was developed. The three methods for determining LAI (digital imaging, LAI-2000, and destructive sampling) were compared. Analysis of the diurnal pattern of transmittance of the cotton canopy showed that the best time for measuring T_imag was around solar noon, because at this time the solar elevation angle is high and remains relatively constant during measurements. Around solar noon, T_imag was in good agreement with T_quan (transmittance measured with a linear quantum sensor). By analyzing the relationships among T_imag, T_quan,and diffuse non-interceptance (DIFN), we determined that T_imag could be used to estimate light attenuation in the cotton canopy at different stages, except for the boll opening stage. In addition, T_imag was saturated at LAI >5. We analyzed the relationship between LAI_dest (LAI measured destructively) and T_imag using data from 2009 and 2010. The R² and SE of the calibration model were 0.8438 and 0.5605, respectively. The ability of T_imag to predict LAI was validated using an independent dataset (2007 data). The determination coefficient and RMSE of the validation model were 0.8767 and 0.4305, respectively. However, the model underestimated LAI as the LAI exceeded 5. The T_imag saturation, which was largely because of errors in image recognition and segmentation, resulted in underestimation of LAI. Intercomparisons of LAI estimates showed that there were small discrepancies and significant correlations among data obtained from digital images, the LAI-2000, and destructive sampling methods. Data from the LAI-2000 was highly consistent with that obtained by destructive sampling. Our results indicate that T_imag data is not as robust as that obtained using other techniques, in that it does not reliably detect non-green leaves, and it is affected by radiation conditions. Nonetheless, it is a simple, reliable, and reproducible approach for general estimates of LAI. The digital camera could be mounted on a tractor or farm vehicle for real-time, non-destructive monitoring of LAI to support field management.