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Different substrate types, such as seagrass, sand, stone and silt, can be identified by detecting substrate reflectivity. In this study, high-resolution remote sensing data of Quickbird were used to map seagrass distribution in the northeast coast of the Xincun Bay. Radiance transfer model in optical shallow water was used to retrieve bottom reflectivity; relationship between hyperspectral band and leaf area index (LAI) was used for processing satellite remote sensing data to retrieve distribution and density of submerged seagrass. Hyperspectral results showed that spectral bands at 550, 650 and 675 nm were sensitive to LAI. Based on the relationship between LAI and hyperspectral bands, Bands 2 and 3 of Quickbird could be used for retrieving seagrass distribution. Satellite results showed that seagrass was mainly distributed in clusters in the northeast coast of the Xincun Bay, tens of metres away from the bank. Density gradient was very clear, with coverage between 40 and 60% occupied by a great part of seagrass bed and with coverage greater than 80% distributed in stripe pattern in the main seagrass bed. The accuracy of seagrass retrieval was more than 80%, and the density of seagrass could also be distinguished evidently. [ABSTRACT FROM AUTHOR]