Outdoor hyperspectral detection can quickly obtain the spectrum information of the sample, but affected by the ambient light and the bidirectional reflectance distribution function of the sample, the collected spectrum cannot accurately reflect the true information of the sample, which has a certain impact on the outdoor detection accuracy. In order to improve the accuracy of outdoor hyperspectral detection, a method of correcting outdoor spectrum using spatial characteristic spectrum was proposed. Walthall, Shibayama, Ross-Li, Roujean and Rahman were used to invert the spatial characteristic spectrum of winter jujube, red grapes and “Xiaobai apricot”, the inverted spatial characteristic spectrum were used to correct the outdoor spectrum. The quality prediction models of darkroom spectrum, outdoor spectrum and correction spectrum were established respectively. The inversion results showed that the spatial characteristic spectrum of the three fruits has good inversion effects, and the inversion errors from low to high are winter jujube, “Xiaobai apricots” and red grapes. The average determination coefficient are 0.957, 0.947, 0.927, and the average errors are 3.56%, 4.90% and 8.23%, respectively; among the five BRDF models, the Walthall model has the best inversion effect, the average determination coefficient and error are 0.949 and 5.33%, respectively. The Ross-li model has the worst inversion effect, the average determination coefficient and error are 0.934 and 6.050%, respectively. The outdoor spectrum correction results show that the noise of the outdoor spectrum is reduced after correction, the spectrum is smooth, and the spectrum trend is consistent with that of the darkroom spectrum; affected by the inversion accuracy, the correction effect of winter jujube spectrum is the best, and the spectrum is smoother, while the correction spectrum of red grapes and “Xiaobai apricot” have more noise. The results of the quality prediction model showed that the accuracy of the quality prediction model of three kinds of fruit was quite different, and the order from high to low are winter jujube, “Xiaobai apricot” and red grape, which might be related to the different quality of fruits; the prediction effects of the models established by the corrected spectrum obtained by the five BRDF models are different, but there is no significant difference; in the prediction model, the prediction model established by darkroom spectrum is the best, and the prediction ability of the model established by modified spectrum is better than that established by outdoor spectrum, indicating that the prediction ability of the model is improved after the outdoor spectrum is corrected. In summary, the BRDF model can better invert the spatial characteristic spectrum of fruits, the corrected spectrum is closer to the darkroom spectrum. The model established by the corrected spectrum is better than the model established by the outdoor spectrum, indicating that the method of using spatial characteristic spectrum to correct the outdoor spectrum is feasible and can provide a new idea for improving the accuracy of outdoor nondestructive testing.