Macroalgae blooms of Ulva prolifera have occurred every summer in the south Yellow Sea since 2007, inducing severe ecological problems and huge economic losses. Genesis and secular movement of green algae blooms have been well moni-tored by using remote sensing and other methods. In this study, green algae were detected and traced by using Geostationary Ocean Color Imager (GOCI), and a novel biomass estimation model was developed from the relationship between biomass measurements and previously published satellite-derived biomass indexes. The results show that the green algae biomass can be determined most accurately with the biomass index of green algae for GOCI (BIGAG), which is calculated from the Rsurf data that had been atmos-pherically corrected by ENVI/QUAC method. For the first time, dynamic changes in green algae biomass were studied over an hourly scale. Short-term biomass changes were highly influenced by Photosynthetically Available Radiation (PAR) and tidal phases, but less by sea surface temperature variations on a daily timescale. A new parameter of biomass changes (PBC), calculated by the ratio of the biomass growth rate to movement velocity, could provide an effective way to assess and forecast green tide in the south- Yellow Sea and similar areas.