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Optical Water Type Guided Benchmarking of Machine Learning Generalization for Secchi Disk Depth Retrieval

Secchi disk depth (SDD) is a widely critical indicator of water transparency. However, existing retrieval models often suffer from limited transferability and biased predictions when applied to optically diverse waters. Here, we compiled a dataset of 6218 paired in situ SDD and remote sensing reflec...

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Principais autores: Bo Jiang, Hanfei Yang, Lin Deng, Jun Zhao
פורמט: Artigo
שפה:Inglês
יצא לאור: MDPI AG 2026-01-01
סדרה:Remote Sensing
נושאים:
Secchi disk depth (SDD)
machine learning (ML)
optical water types (OWTs)
SHAP interpretability
גישה מקוונת:https://www.mdpi.com/2072-4292/18/2/287
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