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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主要な著者:	Bo Jiang, Hanfei Yang, Lin Deng, Jun Zhao
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2026-01-01
シリーズ:	Remote Sensing
主題:	Secchi disk depth (SDD) machine learning (ML) optical water types (OWTs) SHAP interpretability
オンライン･アクセス:	https://www.mdpi.com/2072-4292/18/2/287
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