Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learning - Ruiwu Zhang; Ruru Deng; Jun Ying; Jiayi Li; Yu Guo; Junying Yang; Cong Lei (Science of Remote Sensing)
Fluorescent dissolved organic matter (fDOM), a fluorescent component of dissolved organic matter (DOM), plays a crucial role in tracing pollution pathways in marine environments. While remote sensing has been used to monitor fDOM changes, the impact of multi-source interference has often been overlooked, limiting the accuracy of inversion results. In this study, based on fDOM measurements from Admiralty Bay and from the perspective of optical physical mechanisms, we eliminated atmospheric effects, surface reflection, solar-induced fluorescence (SIF), Raman scattering, and particle absorption from remote sensing reflectance (Rrs(λ)). This preprocessing improved the stability of Rrs(λ), enhancing the reliability of subsequent fDOM inversion. Based on the corrected reflectance, three sensitive wavelengths highly correlated with fDOM were selected. Five machine learning models—Random Forest (RF), XGBoost, Classification and Regression Trees (CART), Gradient Boosting Regression (GBR), and AdaBoost—were then applied for fDOM inversion, with XGBoost achieving the best performance. The inversion results revealed that fDOM concentrations in Admiralty Bay were highest in the western and coastal areas, gradually increasing toward the center, exhibiting a locally clustered distribution. This study demonstrates the effectiveness of combining physical and data-driven methods for fDOM inversion, providing a foundation for long-term monitoring of dissolved organic matter in polar marine environments.