A Temporal–Spatial–Spectral Fusion Framework for Coastal Wetland Mapping on Time-Series Remote Sensing Imagery

Coastal wetland monitoring is essential for protecting marine and terrestrial ecosystems. However, the complex spatial, temporal, and spectral characteristics of these wetlands pose significant challenges for accurate mapping. Coastal wetlands exhibit high spatial heterogeneity due to varied landforms and fluctuating hydrological conditions. Temporal dynamics driven by seasonal cycles and tidal effects, along with spectral similarities across categories and variability within categories, further complicate accurate classification. Existing mapping methods struggle to integrate spatiotemporal and spectral information from time-series data, limiting their ability to model complex boundaries and dynamic changes. To address these challenges, we propose a deep temporal–spatial–spectral interaction learning framework for coastal wetland mapping using time-series remote sensing imagery. The model incorporates a multiscale, multidimensional convolutional module to extract and interact spatial, temporal, and spectral features. A hybrid transformer-convolution module enhances fine-grained feature extraction. While a temporal index extraction module and dual-focus attention module provide prior information, improve the accuracy of challenging wetland category identification. The feature fusion and adaptive classification module, dynamically assigning weights based on the importance of temporal, spatial, and spectral features for optimal information aggregation boosting classification performance. We validated the model using Sentinel-2 time-series multispectral datasets from the Yellow River Estuary, Yancheng coastal wetlands, and Hangzhou Bay. Experimental results demonstrate competitive performance: the overall accuracy reaches 98.07% for Yellow River Estuary, 91.55% for Yancheng, and 97.82% for Hangzhou Bay.