With the development of the Industrial Internet of Things (IIoT) and the proposal of smart water conservancy, the integration of the Internet of Things (IoT), edge computing, and computer vision for hydrological information monitoring has become a trend in water conservancy. Difficult to provide comprehensive hydrological data from hydrological observation stations, hydrological forecasting in uninformative basins has become one of the research hotspots. water extraction can provide support for many hydrological monitoring. However, The water is difficult to extract when the water in the near-water image has reflection and water surface ripples. We propose a multi-condition water extraction method based on superpixel segmentation under cloud-edge integration architecture. Firstly, based on the color characteristics of the water and the surrounding vegetation, we propose a reflection processing mechanism. Secondly, we propose a mechanism based on superpixel segmentation for the effect of reflection on water extraction. Then, we propose a contour correction mechanism. Finally, the flood-filling algorithm is used to extract the water. We compare the traditional image segmentation algorithm with our proposed segmentation method. The results show that the proposed method outperforms other algorithms in all evaluation metrics.