Study on remote sensing inversion and temporal-spatial variation of Hulun lake water quality based on machine learning.
Hulun Lake
Landsat 8 OLI
Random Forest
Temporal–spatial dynamics
Water quality retrieval
Journal
Journal of contaminant hydrology
ISSN: 1873-6009
Titre abrégé: J Contam Hydrol
Pays: Netherlands
ID NLM: 8805644
Informations de publication
Date de publication:
12 Dec 2023
12 Dec 2023
Historique:
received:
30
10
2023
revised:
27
11
2023
accepted:
06
12
2023
medline:
16
12
2023
pubmed:
16
12
2023
entrez:
15
12
2023
Statut:
aheadofprint
Résumé
Hulun Lake is facing significant water quality degradation, necessitating effective monitoring for safety. Traditional methods lack the necessary spatial and temporal coverage, underscoring the need for a remote sensing model. In this study, we utilized the Landsat 8 OLI dataset, incorporating cross-section monitoring and field sampling data comprehensively. Employing the random forest algorithm, we constructed a remote sensing inversion model for six water quality parameters in Hulun Lake: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH
Identifiants
pubmed: 38101229
pii: S0169-7722(23)00152-3
doi: 10.1016/j.jconhyd.2023.104282
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
104282Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare no conflict of interest.