Seagrasses are ecologically important yet highly threatened blue carbon ecosystems that play a critical role in environmental protection, biodiversity conservation, and carbon sequestration. However, their spatial heterogeneity and dynamic temporal behavior pose challenges to accurate mapping and long-term monitoring. The availability of publicly accessible satellite images with high spatial and temporal resolution, and advances in machine learning, have gradually expanded seagrass geospatial research and led to more accurate and robust image classifications. This study evaluated the performance of traditional and machine learning methods for seagrass and benthic habitat mapping using clear and sun-glinted 3-meter resolution PlanetScope imagery. Classification accuracy metrics were compared across multiple algorithms and varying image quality, using two different reference datasets. Results indicated that the Maximum Likelihood Classification and Support Vector Machine Classification achieved the highest overall accuracy and kappa statistics for the clearest image used, the 8-band PlanetScope image acquired on February 16. As expected, the application of the sun glint correction procedure improved classification accuracies for lower-quality images, particularly for the Random Forest Classification, which showed consistent and pronounced gains after deglinting. These findings demonstrate the potential of PlanetScope images for seagrass and benthic mapping, keeping in mind that careful image selection remains essential due to the imagery’s inherent sensitivity to sun glint and other radiometric inconsistencies affecting classification performance. In the absence of optimal or clear images, scenes with lower image quality may still be effectively utilized with the application of radiometric correction procedures such as sun glint removal.