Drone-based remote sensing offers a scalable pathway for mapping seagrass, but its effectiveness in turbid, intertidal environments with patchy vegetation remains poorly established. We developed and evaluated a reproducible drone-based workflow across three contrasting tropical intertidal seagrass meadows in northern Australia, spanning dense multispecies, sparse sandy, and moderately vegetated muddy systems. These habitat types are increasingly vulnerable to climate-driven water quality decline and anthropogenic disturbance. High-resolution orthomosaics and low-altitude spot-check imagery were used to quantify seagrass extent, percent cover, and species composition. A UNet++ semantic segmentation model mapped seagrass extent in fragmented, low-cover environments, performing best in moderate-to-high cover areas. Bayesian hurdle models showed that random and transect-based spot-check sampling designs produced comparable cover estimates across all sites. Rolling RMSE analyses indicated estimates stabilised at ~50 spot-checks per location. Detection-limit analyses revealed that seagrass detectability depends strongly on spatial resolution, percent cover, species morphology, and sediment background; fine-leaved species such as Halodule uninervis were difficult to detect below ~10% cover even at high resolutions. These findings offer transferrable guidance on sampling effort, detectability limits, and analytical approaches, with direct relevance to biodiversity monitoring, blue carbon accounting, and ecosystem state assessment under a changing climate.