Detecting and monitoring surface water has received much attention in recent decades. Surface water is one of the most critical water resources for both human and ecological systems. Remote sensing technology has made it possible to have accurate and frequent updates of surface water. We propose a remote sensing multisensor fusion system using optical data including Landsat-8 and Sentinel-2 and RADAR data including Sentinel-1 for water body extraction. Using a data fusion approach, the spatial resolution of multispectral images increased from 30 to 10 m, and the spectral information of Landsat-8 and Sentinel-2 were preserved. Then, all features extracted from the high spatial resolution images, water index maps, and Sentinel-1 dataset were combined as a stacked feature space. The new data were subsequently classified by support vector machine, neural network, and random forest. Finally, all classifiers’ outputs were integrated using a weighted majority voting decision fusion strategy, which significantly improved surface water extraction. Our study illustrates the ability of remote sensing multisensory fusion, water indices, and decision fusion for water body extraction.