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<i>Ulva prolifera</i> and <i>Sargassum</i> are two common floating macroalgae in China’s coastal algal bloom events. <i>Ulva prolifera</i> frequently emerges concomitantly with <i>Sargassum</i> outbreaks, thereby presenting challenges to the monitoring of algal blooms, thereby presenting challenges to the monitoring of algae. To tackle the challenge of differentiating between <i>Ulva prolifera</i> and <i>Sargassum</i>, this study employs Sentinel-2 MSI data for spectral analysis. Notably, significant disparities in the Remote Top of Atmosphere Reflectance (Rtoa) between <i>Ulva prolifera</i> and <i>Sargassum</i> are observed. This study proposes a random forest-based algorithm for discriminating between <i>Ulva prolifera</i> and <i>Sargassum</i> in the regions of the Yellow Sea and East China Sea. The algorithm introduced in this study attains remarkable accuracy in distinguishing <i>Ulva prolifera</i> and <i>Sargassum</i> within Sentinel-2 MSI data, achieving identical F1 scores of 99.1% for both. Moreover, when tested with GF-1 WFV data, the algorithm showcases outstanding performance; this demonstrates the algorithm’s robustness and its ability to mitigate the uncertainty linked to threshold selection. Simultaneously, a comparative analysis of algae distribution was conducted for both 2017 and the period from January to May 2023. Experimental results indicate that the algorithm exhibits high accuracy in distinguishing between <i>Ulva prolifera</i> and <i>Sargassum</i>. This capability will significantly enhance the monitoring of large algae in maritime regions; this holds crucial theoretical significance and offers substantial practical value in the realm of marine ecological conservation.