Urban black and odorous water (BOW) is a serious global environmental problem. Since these waters are often narrow rivers or small ponds, the detection of BOW waters using traditional satellite data and algorithms is limited both by a lack of spatial resolution and by imperfect retrieval algorithms. In this paper, we used the Chinese high-resolution remote sensing satellite Gaofen-2 (GF-2, 0.8 m). The atmospheric correction showed that the mean absolute percentage error of the derived remote sensing reflectance (<inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula>) in visible bands is 25.19%. We first measured <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> spectra of two classes of BOW BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2 and ordinary water in Shenyang. Then, in situ <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> data were converted into <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> corresponding to the wide GF-2 bands using the spectral response functions. We used the converted <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> data to calculate several band combinations, including the baseline height, <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula>(green) <inline-formula> <tex-math notation="LaTeX">- R_{\mathrm {rs}} </tex-math></inline-formula>(red))/(<inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula>(green) <inline-formula> <tex-math notation="LaTeX">+ R_{\mathrm {rs}} </tex-math></inline-formula>(red), and the color purity on a Commission Internationale de L'Eclairage (CIE) chromaticity diagram. The color purity was found to be the best index to extract BOW from ordinary water. Then, <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> (645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing.