Data and code for "Evaluating the cross-lake transferability limits of machine learning models for Sentinel-3 inland water Chlorophyll-a retrieval"

This archive contains the data and MATLAB code used to reproduce the analyses, model evaluations, tables, and figures for the manuscript: “Evaluating the cross-lake transferability limits of machine learning models for Sentinel-3 inland water Chlorophyll-a retrieval” The study evaluates the transferability of machine-learning Chlorophyll-a retrieval models trained on the Western Basin of Lake Erie and tested on Saginaw Bay of Lake Huron using Sentinel-3 OLCI remote-sensing reflectance and paired in situ water-quality observations. The archive includes raw and processed data, trained MATLAB model files, MATLAB scripts, classification-specific working files, and documentation needed to reproduce the reported model evaluations. The package supports the global/no-classification model and three classification-based adaptive modelling strategies.