Since the beginning of 21^st century, the extent and frequency of algal blooms have been increasing significantly, which is driven by the intensifying impact of human activities on oceanic environment. With its wide coverage and high revisit frequency, satellite remote sensing technology has become an essential tool and a key direction in the detection, monitoring, and forecasting of red tides. Although various remote sensing algorithms for red tide detection have been developed and widely applied, most of them focus on specific regions or independent red tide events, thus no consensus has been reached on methods with best performance. Using multiple ocean color remote sensing data with different spatial and temporal resolutions (MODIS, VIIRS, and GOCI, etc.), this study selects globally seven waters prone to red tide with different latitude circles and water types, i.e., five coastal waters around Qinhuangdao, Zhejiang, and in Bohai Bay, Arabian Sea, West Florida Shelf, and two open waters in Sargasso Sea, and Ross Sea. Three commonly used remote sensing detection methods are selected, and their detection accuracy and regional adaptability are analyzed and compared. Knowing their advantages of each algorithm and their sensitivity and speciality across geographic regions and ocean color data, this study could offer reference for selecting and optimizing region-specific red tide remote sensing detection algorithms and also provide new insights for developing brand new algorithms or improving detection ability in remotely detecting red tide.