Visible/near-infrared (VNIR) spectroscopy and spectral scattering are based on different sensing principles, and they have shown different abilities for predicting apple fruit firmness and soluble solids content (SSC). Hence the two techniques could work synergistically to improve the quality prediction of apples. In this research, VNIR spectroscopic and spectral scattering data for the wavelength range of 460-1,100 nm were collected for 6,631 apples of 'Delicious', 'Golden Delicious' and 'Jonagold' cultivars during the 2009 and 2010 harvest seasons and for three months after the refrigerated air storage. Partial least squares models were developed for each sensor and their combination to predict the fruit firmness and SSC for both single-year and cross-year data sets. The spectral scattering technique generally performed better in predicting firmness, whereas the VNIR technique was superior for prediction of SSC. Overall, the data fusion of the two sensors produced significant improvements (p<0.05) for prediction of the firmness and SSC than individual sensors. Cross-year prediction results for firmness and SSC were lower, compared with prediction results for each year. However, the cross-year prediction model for SSC was more robust and less sensitive to the harvest-year effect, compared to the firmness prediction model. Sensor fusion can provide more robust and accurate firmness and SSC assessment for apples.