Abstract: Urban black and odorous water (BOW) bodies are extreme water pollution phenomena that threaten human production, life, and the health of aquatic ecosystems. Timely and accurate identification of the spatial and temporal distribution of BOW using remote sensing techniques plays a crucial role in understanding the progress of their treatment and advancing water pollution control and management. In this study, taking Guangzhou City as the study area, several BOW identification models were constructed based on the spectral differences between BOW and normal water in Gaofen (GF) image with high spatial resolution. Then an automatic threshold selection algorithm was employed for rapid threshold selection in the BOW models, enabling the detection of BOW bodies. The results showed that the overall identification accuracy of BOW bodies could reach up to 96.8%. In terms of the spatial distribution of BOW identification, the constructed model e1 had a relatively low misclassification probability and achieved good recognition performance, especially in small rivers and streams. The errors in consistency between surface water quality data and satellite images, high-resolution image fusion methods, and the existence of mixed pixels may be the causes of misidentification for BOW. The BOW model based on high spatial resolution images with automatic threshold selection can achieve fast and efficient detection of BOW bodies, providing effective support for BOW treatment and the precise management of the urban water environment.