A digital imaging system containing a calibration target, an image capture device, and a mathematical model to estimate spectral reflectance factor was treated as a spectrophotometer and as such subject to systematic and random errors. The systematic errors considered were photometric zero, photometric linear and nonlinear scale, wavelength linear and nonlinear scale, and bandwidth. To diagnose and correct the systematic errors in a spectral imaging system, a technique using multiple linear regression as a function of wavelength was employed, based on the measurement and image based estimating of several image verification targets. Based on the stepwise regression technique, the most significant diagnosed systematic errors were photometric zeros, photometric linear scale, wavelength linear scale, and bandwidth errors. The performance of spectral imaging after correction of the estimated spectral reflectance, based on the modeling result, was improved on average 25.3% spectrally and 16.7% colorimetrically. This technique is suggested as a general method to improve the performance of spectral imaging systems.