A hybrid neural network for mangrove mapping considering tide states using Sentinel-2 imagery

Mangroves, as cradles of biodiversity and blue carbon reservoirs, are facing survival challenges due to climate change and anthropogenic disturbance. Precise and rapid mapping of mangrove forests has thus become highly relevant, which can provide essential information to support the conservation practices of such blue carbon resources. Existing machine learning algorithms for mangrove mapping are incapable of delivering precise cartographic solutions under dynamic tidal conditions because of poor transferability. This study developed a generalized approach for large-area mangrove mapping using a hybrid neural network integrated with a vision transformer to effectively capture representative features. To adapt mangrove mapping to the variety of tidal conditions, a vision transformer architecture was developed by encoding the fusion of three Sentinel-2 bands: Green, NIR, and SWIR. The ground truth dataset for the year 2021 was created from the composited Sentinel-2 images after interpreting Google Earth and drone images, which comprised 88,645 training samples (256 × 256 pixels per sample) and 24,969 test samples. We selected 30 coastal counties as test dataset in China to evaluate the effectiveness of the proposed network and produced a 10 m mangrove map that reported a total mangrove area of 28,006.24 ha in China in 2021, yielding an overall accuracy (OA) of 95.91 %. Compared to existing data products, Global Mangrove Watch 3.0, ESA WorldCover V200 and HGMF, our method outperformed the second-best product HGMF in mixed tide regions, by a margin of 9.19 % in OA and by 9.36 % in mean F1 score. Despite fluctuations in tide levels captured by Sentinel-2 imagery, the proposed method consistently yielded robust mangrove mapping results, highlighting effective derivation of tidal information. In comparison with previous mapping methods, the superior efficacy of the proposed network is distinctly discernible in distinguishing and delineating mangrove ecosystems in mixed tide regions, presenting prospects for improved monitoring of mangroves at regional and global scales. • Proposed a hybrid neural network for large-area mangrove mapping in China. • The proposed neural network demonstrated transferability across various tidal states. • Proposed network introduced edge guidance to enhance delineation of forest boundaries. • Significantly improved accuracy of mangrove mapping in mixed-tide areas was achieved.