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    Comparative analysis of Sentinel-2 and PlanetScope imagery for chlorophyll-a prediction using machine learning models by Eden T. Wasehun, Leila Hashemi Beni, Courtney A. Di Vittorio, Christopher M. Zarzar, Kyana R.L. Young

    Published 2025-03-01
    “…We developed three scenarios to select key features, aiming to optimize the retrieval of Chl-a for a lake in North Carolina, USA. Utilizing five machine learning models, namely linear regression (LR), least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR), we constructed inversion models. …”
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