Satellite tracking of long-term spatiotemporal dynamics in China’s largest seagrass meadow

Seagrass meadows are vital coastal ecosystems that provide essential ecological functions but are highly vulnerable to human disturbances and environmental change. Effective monitoring is therefore critical for understanding their long-term dynamics and supporting conservation efforts. Satellite remote sensing provides an efficient approach for large-scale and long-term observation of seagrass ecosystems; however, accurate monitoring remains challenging due to tidal variability, seasonal growth dynamics, and complex underwater environments. This study focuses on the Caofeidian (CFD) seagrass meadow, the largest continuous seagrass habitat in China. Using Sentinel-2 Multispectral Instrument (MSI) imagery, we quantified its spatiotemporal dynamics from 2016 to 2024. A standardized workflow integrating pixel-level classification, image composition, and trend analysis was developed to characterize long-term variation patterns and identify areas associated with degradation, stability, and recovery. The results reveal a persistent net decline in seagrass extent over the past decade, despite localized recovery observed during the period of restoration activities after 2019. Five variation classes were identified: Significant Degradation (SD), Slight Degradation (SID), Stable (ST), Slight Recovery (SIR), and Significant Recovery (SR), accounting for 5.2 %, 15.5 %, 55.8 %, 22.9 %, and 0.6 % of the meadow area, respectively. Severe losses in several localized areas showed strong spatial and temporal associations with sustained human activities, including land reclamation, clam harvesting, and waterway development, whereas recovery patterns were mainly observed in regions with targeted restoration backgrounds. This study provides the first region-wide assessment of long-term seagrass spatiotemporal dynamics in CFD and demonstrates the potential of multi-temporal satellite observations for monitoring coastal seagrass ecosystems and supporting adaptive conservation management.