Sentinel-2 mapping of a turbid intertidal seagrass meadow in Southern Vietnam

Seagrass ecosystems are crucial to the carbon cycle, biodiversity, fisheries, and erosion prevention. However, there are gaps in mapping seagrass in tropical coastal waters due to turbidity and constantly changing tidal dynamics. To tackle such issues, tidal heights and reflectance of 33 Sentinel-2 images were used to calculate a multitemporal composite of Sentinel-2 images, which was classified using the Random Forest classifier. UAV images classified with Object-based image analysis provided ground truth for training and validation. The effectiveness of water column correction and using the multitemporal composite was evaluated by comparing overall classification accuracy. As a result, applying water column correction improved classification accuracy from 80.4% to 80.6%, while using the multi-temporal composite improved accuracy to 88.6%, among the highest accuracy achieved for seagrass classification in turbid waters. The combined protocol could lead to advances in quantifying seagrass distribution in the tropical coastal waters and their carbon sequestration capacity, resolving significant uncertainties in blue carbon estimation.

Automated summary

Seagrass ecosystems play a crucial role in the carbon cycle, biodiversity, fisheries, and erosion prevention. Mapping seagrass in tropical coastal waters is challenging due to turbidity and changing tidal dynamics. This study used a combination of Sentinel-2 images, tidal heights, and reflectance to improve seagrass classification accuracy, resulting in one of the highest accuracies achieved in turbid waters. The findings contribute to quantifying seagrass distribution and carbon sequestration capacity in tropical coastal waters, addressing uncertainties in blue carbon estimation.