Land surface water is one of the most vital resources on Earth for human survival, and it is necessary to distinguish water bodies from nonwater features. Remote sensing techniques are among the most widely used approaches for monitoring water resources; many waterbody delineation methods have been proposed, and multiband spectral water indices are among the most popular. These methods utilize blue, green, near-infrared (NIR), middle-infrared, and shortwave infrared bands but do not involve the red band. A waterbody delineation index is introduced, i.e., the deeply clear waterbody delineation index (DCWDI), which is based on the reflectance of the red and NIR bands. In NIR-red spectral space, the distance between deeply clear water pixels and the coordinate origin, O, is less than that of other land cover types. This method can use the distance between any point E and the coordinate origin O to differentiate deeply clear water pixels from nonwater pixels, i.e., objects near point O are always deeply clear water bodies or extremely wet regions. The accuracy and robustness of the DCWDI are tested using Landsat 8 operational land imager images of Hongjiannao Lake, Qinghaihu Lake, and Lingao Reservoir. The performance of the DCWDI is compared with that of the normalized difference water index (NDWI), automated water extraction index (AWEI), and the modified NDWI (MNDWI). The net shoreline movement (NSM) and the area errors between delineated water areas and the “true” areas of water bodies are adopted to evaluate the accuracies of the four classifiers. The mean   |  NSM  |   values from the DCWDI of Hongjiannao Lake, Qinghaihu Lake, and Lingao Reservoir are 17.033, 75.108, and 11.021 m, respectively, which are smaller than those from the MNDWI (34.641, 149.308, and 19.647 m), NDWI (71.607, 164.503, and 22.151 m), and AWEI_nsh (19.957, 113.119, and 11.126 m). The average of   |  NSM  |   and area error from the DCWDI are smaller than those from the other three classifiers. These results show that the lake boundaries derived from the DCWDI are spatially similar to the “true” lake boundaries, and the DCWDI, which is obtained from NIR-red spectral space, can be applied to delineate and detect the changes in information on deeply clear water. The spatial location information contained within NSM can validate the accuracy of a classifiers’ ability to extract water bodies.