2024
DOI: 10.3390/w16243615
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An Interpretable CatBoost Model Guided by Spectral Morphological Features for the Inversion of Coastal Water Quality Parameters

Baofeng Chen,
Yunzhi Chen,
Hongmei Chen

Abstract: Chlorophyll-a (Chla) and total suspended solid (TSS) concentrations are important parameters for water quality assessment, and in recent years, machine learning has been shown to have great potential in this field. However, current water quality parameter inversion models lack interpretability and rarely consider the morphological characteristics of the spectrum. To address this limitation, we used Sentinel-3 OLCI data to construct an interpretable CatBoost model guided by spectral morphological characteristic… Show more

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