CCAV-10m: An Annual Spatiotemporal China's Coastal Wetland Vegetation Dataset Integrating Sentinel-1/2 Observations via Deep Learning

Abstract. Coastal wetland vegetation plays a vital role in shoreline protection and ecosystem management, highlighting the need for accurate and high-resolution mapping of these unique and vulnerable habitats. Here, we present CCAV-10m, the first publicly available annual species-level coastal wetland dataset for China at 10 m resolution (2016–2023). This dataset was generated using a novel phenology-guided coastal wetland vegetation classification network (P_SVCN), which integrates Sentinel-1/2 satellite imagery with extensive in situ observations. Validation based on 4,668 in situ samples confirms that P_SVCN delivers strong classification performance, achieving an overall accuracy of 0.916 and a Kappa coefficient of 0.898. Spatiotemporal analysis of CCAV-10m reveals that Suaeda spp. is the dominant vegetation type, followed by S. alterniflora, whose coverage nearly equals the combined extent of P. australis, mangroves, S. mariqueter, and T. chinensis. Notably, this work fills critical gaps in both spatial detail and temporal consistency across existing coastal wetland datasets, demonstrating the effectiveness of deep-learning-based fusion of optical and SAR data for high-resolution vegetation mapping. Regular updates to CCAV-10m will support long-term coastal wetland research, enhance invasive species monitoring, and inform wetland restoration and precision management efforts. The CCAV-10m dataset is openly accessible at https://doi.org/10.57760/sciencedb.31077 (Li et al.,2025).