Cross-Regional and Interpretable Marine Mucilage Monitoring Using Spectral Indices and SHAP

The recurring phenomenon of marine mucilage in the Mediterranean region highlights the urgent need for effective environmental monitoring of marine ecosystems under stress. This study leverages Sentinel-2 multispectral imagery and incorporates a wide range of spectral indices to develop a CNN-based classification model for marine mucilage detection and mapping. The model was trained and validated on data of the Sea of Marmara, acquired in 2021, and tested on data from the Adriatic Sea, acquired in August 10, 2024, to evaluate its generalizability across geographically distinct regions. A SHAP-based explainability analysis was employed to assess the contributions of individual spectral indices to model predictions. The results highlight the robustness of key indices in mucilage detection and reveal new insights into their applicability across diverse environmental contexts, providing a foundation for broader monitoring applications in the Mediterranean.