Research on Long-Term Time Series Chlorophyll Concentration Inversion Method Based on Multisensor Consistency Transformation

Recurrent algal blooms in coastal regions have become a pressing environmental concern, significantly impacting the ecological integrity of the Bohai Sea due to their sudden occurrence and rapid development. Addressing this issue neces sitates high-resolution and continuous remote sensing methods for dynamic monitoring. This study confronts two challenges: the inconsistency in band configurations and spectral widths across multi-source satellite sensors when generating long-term obser vation datasets, and the limited applicability and low accuracy of traditional empirical models for Chlorophyll-a (Chl-a) retrieval under complex optical conditions. To overcome these limitations, a harmonized long-term dataset was developed using the consis tent spectral response characteristics of geostationary ocean color imager (GOCI)-I and GOCI-II sensors. In addition, a Chl-a inver sion framework was proposed by integrating a Quasi-analytical algorithm version 6 (QAA-v6)-optimized physics-informed neural network (QPINN) with a spatiotemporal attention mechanism. A weighted transformation approach was employed to standardize reflectance data from both sensors, enabling a unified dataset with consistent spectral and spatial parameters. Spatiotemporal features were extracted using a Transformer architecture and combined with QPINN to suppress anomalies, enforce physical constraints, and improve model generalization. The resulting QPINN-Transformer model achieved a high inversion accuracy, with a coefficient of determination R² = 0.91,MAE = 0.3750. Long term application of the model from 2014 to 2020 revealed a strong spatial association between Chl-a concentrations and the central hypoxic zone in the Bohai Sea. These findings offer methodolog ical advancements for the dynamic evaluation of eutrophication, inform land-based pollution mitigation strategies, and support assessments of marine carbon sequestration potential.