The impact of feature engineering and an optimisation framework for ocean colour machine learning

The study evaluates how feature engineering (FE) affects machine learning models for ocean colour data, proposing a seven‑step optimisation framework that includes band selection, scaling, normalisation, index extraction, PCA, and feature scaling. Applied to Sentinel‑3 OLCI observations, the framework improves model accuracy for estimating Chlorophyll‑a and Secchi disk depth, achieving higher R values and lower mean absolute errors compared to standard algorithms. However, the optimal FE varies across targets and models, indicating that FE optimisation must be tailored to each application.

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