The surface spectral reflectance of the object is regarded as the fingerprint of its color. At the same time, it is also important to characterize the physical and chemical properties of substances. Spectral imaging technology based on spectral reconstruction can overcome the dependence of RGB images on imaging conditions. Meanwhile, it can effectively improve the spectral image's spatial resolution and acquisition efficiency and reduce equipment costs. Different from the principle of multispectral cameras, spectral imaging based on spectral reconstruction first captures the digital images of the object using a digital imaging system, and then the corresponding spectral images are reconstructed using spectral reconstruction methods. Current research has made great achievements in the laboratory; however, dealing with rapidly changing light sources, illumination, and imaging parameters in an open environment presents significant challenges for spectral reconstruction. This is because a spectral reconstruction model established under one set of imaging conditions is not suitable for use under different imaging conditions. To deal with the challenges, we explored the feasibility of spectral reconstruction based on camera raw response prediction in this study. In the proposed method, the camera raw response of the training dataset under specific imaging conditions is first predicted via the camera imaging model, then the spectral reconstruction algorithm is applied to spectrally characterize the digital camera based on the training dataset, at last, the spectral reflectance of the testing target is reconstructed from the captured image under the same imaging condition. The study tested the prediction results of raw response values for ColorChecker SG 140 color cards in a closed-light box environment and an outdoor open environment under five different combinations of exposure time and ISO. Spectral reconstruction tests were also conducted using the ColorChecker SG 140 color card for the ColorChecker 24 color card. In the closed lightbox and under different imaging conditions, the average RMSE(%) for reconstructing the true photographic response values using the predicted response values was 4.02, with an average CIEDE2000 color difference of 5.3. In the outdoor open environment and under different imaging conditions, the average RMSE(%) was 3.2, with an average CIEDE2000 color difference of 4.5. The experimental results show that the proposed method still has good reconstruction accuracy in outdoor environments, providing a feasible reference solution for spectral reconstruction in an open environment. In addition, we find that the proposed method is sensitive to the spectral reconstruction algorithms used, and different algorithms have different performances in spectral and chromaticity aspects.