Taking the images of Landsat TM, ALOS AVNIR-2, CBERS-02B CCD and HJ-1 CCD as the experimental data, for increasing the differences among shaded area, bright area and water further, the present paper construed a novel vegetation index-Shaded Vegetation Index(SVI), which can not only keep the absolute differences among bright area, shaded area and water area in the near-infrared band, but also can enlarge NDVI, eliminate the possible mixes, and change the histogram “skewed” phenomenon of NDVI, so the vegetation index value is closer to normal distribution, and more in line with the filed condition; this new index was applied to the surface features of large difference of the near-infrared radiation characteristics. Verified by accuracy assessment for the bright area, shaded area and water area recognition effects with SVI, it was showed that the overall classification accuracies of these images were up to 98.89%, 100%, 97.78% and 97.78% respectively, with the overall Kappa statistics of 0.983 3, 1, 0.966 7, and 0.966 7, indicating that SVI has excellent detection effects for bright area, shaded area and water area; the statistical comparison of sub-images between SVI and NDVI also illustrated the reliability and effectiveness of SVI, which can be applied in the shadow removal for remote sensing images.