An Interpretable Transformer-Based Framework for Monitoring Dissolved Inorganic Nitrogen and Phosphorus in Jiangsu–Zhejiang–Shanghai Offshore

Anthropogenic increases in nitrogen and phosphorus inputs have intensified coastal water pollution, leading to economic losses and even threats to human health. Dissolved Inorganic Nitrogen (DIN) and Dissolved Inorganic Phosphorus (DIP), as key indicators of water quality, are essential for formulating environmental protection strategies. While deep learning has advanced the retrieval of these nutrients in coastal waters, existing models remain constrained by limited accuracy, insufficient interpretability, and poor regional transferability. To address these issues, we developed a Transformer-based model for retrieving DIN and DIP in the Jiangsu-Zhejiang-Shanghai (JZS) Offshore, integrating satellite observations with reanalysis data. Our model outperformed previous studies in this region, achieving high retrieval accuracy for DIN (R2 = 0.88, RMSE = 0.16 mg/L, and MAPE = 33.69%) and DIP (R2 = 0.85, RMSE = 0.007 mg/L, and MAPE = 31.59%) with strong interpretability. Based on this model, we generated a long-term (2005–2024) dataset, revealing clear seasonality and spatial patterns of DIN and DIP. Specifically, the concentrations have a distinct seasonal cycle with winter minima and autumn maxima, as well as estuarine-to-offshore decreasing gradient. Water quality assessment further showed that the areal extent of medium-to-high eutrophic waters increased by 3.94 × 102 km2/yr (2005–2016) but decreased by 4.45 × 102 km2/yr (2016–2024). Overall, the proposed Transformer-based framework provided a robust, accurate, and interpretable tool for nitrogen and phosphorus nutrient retrieval, supporting sustainable management of marine water quality in the JZS coastal ecosystems.