Visual and Near-infrared (VIS-NIR) reflectance spectroscopy had been used widely in monitoring agricultural pollution in recent years, however, it was rarely applied in monitoring the contamination of heavy metal in orchards. In the present paper, Newhall navel orange (Citrus sinensis [L.] Osbeck cv. Newhall) were cultivated in the potted soil contaminated with cadmium (Cd) at different levels, and the spectral reflectance and Cd content in the leaves were measured simultaneously at different growing seasons, which then were used to establish the prediction model by partial least squares regression (PLSR) based on spectral reflectance and by linear regression based on spectral index. The results showed that Cd was more easily transferred to and cumulated in the new leaves, and this phenomenon was more obvious in heavily contaminated soils with Cd. Blue shift in red edge was found in the band of 700-730 nm in the new leaves, however, no such phenomenon was found in the old leaves. The coefficient of determination (R²) of linear regression model based on spectral index was nearly 0. 8, while the PLSR model had a better result in predicting Cd content in the new leaves than the linear regression with R²CV of approximately 0.9. Furthermore, the standard normal variate transformation(SNV) in spectral preprocessing can improve the precision significantly in PLSR model. These results suggest that the VIS-NIR method has a great potential in monitoring heavy metal pollution in the navel orange.