An enhanced deep learning approach to assessing inland lake water quality and its response to climate and anthropogenic factors

An enhanced deep learning approach to assessing inland lake water quality and its response to climate and anthropogenic factors

[Display omitted] •An enhanced deep learning approach was proposed to remotely assess water quality.•The approach outperformed other candidate algorithms in water quality mapping.•Merged OLI/MSI data supports water quality detecting with repeat cycle 

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of hydrology (Amsterdam) 2023-05, Vol.620, p.129466, Article 129466
Hauptverfasser: Guo, Hongwei, Zhu, Xiaotong, Jeanne Huang, Jinhui, Zhang, Zijie, Tian, Shang, Chen, Yiheng
Format: Artikel
Sprache:eng
Schlagworte:
Canada
chlorophyll
climate
Deep learning
dissolved oxygen
environmental management
hydrology
lakes
Landsat
meteorological data
organic carbon
phosphorus
reflectance
Remote sensing
Sentinel-2
surface temperature
time series analysis
total nitrogen
turbidity
Water quality
watersheds
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:[Display omitted] •An enhanced deep learning approach was proposed to remotely assess water quality.•The approach outperformed other candidate algorithms in water quality mapping.•Merged OLI/MSI data supports water quality detecting with repeat cycle 
ISSN:0022-1694
DOI:10.1016/j.jhydrol.2023.129466