A multi-pathway feature engineering framework for total phosphorus retrieval from field hyperspectral data: a case study of Dianshan Lake

Eutrophication in inland lakes, driven by excessive total phosphorus (TP), necessitates robust monitoring methods. While hyperspectral remote sensing holds promise, accurate TP retrieval is hindered by its non-optically active nature and the complex spectral interplay in water bodies. This study aimed to develop a novel, interpretable framework to overcome these challenges by synergistically integrating diverse spectral features for improved TP estimation from field hyperspectral data. The key innovation of this study was a multi-pathway feature engineering framework that strategically merged three complementary information streams: spectral indices grounded in bio-optical theory via proxies for suspended solids (SS) and coloured dissolved organic matter (CDOM), statistically optimized features from Lasso regression, and machine learning-derived predictors from XGBoost. This fusion reconciled physical interpretability with data-driven predictive power. Applied to Dianshan Lake, the framework achieved a superior prediction accuracy (R2 = 0.81) compared to any single pathway. Furthermore, hierarchical optimization distilled the fused features into a compact core set of only 8 predictors, retaining over 90% of the model’s performance and highlighting the framework’s efficiency. The study demonstrates a scalable and physiochemically insightful approach for advancing hyperspectral retrieval of non-optically active water quality parameters.