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River algal blooms can be estimated by remote sensing reflectance

Bibliographic Data

ID15548657
AuthorsTonghui Huang (Chinese Research Academy of Environmental Sciences, corresponding author), Rui Xia (0000-0001-7984-0579, Chinese Research Academy of Environmental Sciences), Kai Zhang (0000-0002-7401-2931, Chinese Research Academy of Environmental Sciences), Yan Chen (0000-0003-4119-9688, Chinese Research Academy of Environmental Sciences), Yuanxin Ren (Northwest University), Jinxi Song (0000-0001-9838-8063, Northwest University), Yao Wang (0000-0002-7024-4233, Chinese Research Academy of Environmental Sciences), Chengjian Liu (Institute of Geographic Sciences and Natural Resources Research)
Year2024
Volume19
Issue10
Pages104065-104065
Publication date2024-08-16
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ad7043
OpenAlexW4401629943
LanguageEN
References cited82

River eutrophication is difficult to diagnose and estimate quantitatively because of its complex degradation mechanism in large river systems. Conventional monitoring and modeling methods are limited to accurately revealing the evolution process and trends of river aquatic organisms. In the present study, based on HJ-1A/1B CCD sensor, combined with genetic algorithm (GA) and regression tree (GART), a remote sensing inversion prediction model was established; the model can estimate algal blooms in the Han River affected by China’s Middle Route of the South-to-North Water Diversion Project (SNWTP). During the outbreak of algal blooms, the near-infrared band reflectance evidently increased between 2009 and 2015, with increasing algal density. The algal density in the downstream of the Han River has a nearly synchronous positive change with the reflectance in the B4 (near-infrared) band and a nearly synchronous reverse change with the B1 (blue) band. B1 and B4 screened by GA reduced redundancy by 14%, leading to a good prediction performance ( R 2 = 0.88). According to GART and partial dependence analysis, the B4 band is a crucial characterization factor of algal blooms in the Han River. When the remote sensing band was in the range of B1 ⩾ 0.085 and B4 ⩽ 0.101, the algal density was lower than 0.15 × 10 7 cells l −1 , indicating no algal bloom in the downstream of the Han River. When B4 was >0.103 and B1 ⩽ 0.076, algal density was higher than 1 × 10 7 cells l −1 and algal blooms were very likely to occur. These findings could provide a scientific reference for diagnosing and predicting large-scale water ecological degradation in similar watersheds

Algal bloom · Biology · Bloom · Eutrophication · Hydrology (agriculture · Nutrient · Phytoplankton · Red tide · Reflectivity · Remote sensing · Environmental Science · Fish Ecology and Management Studies · Water Quality Monitoring and Analysis · Water Quality Monitoring Technologies · Ecology · Geology · Oceanography

  • Characterization and causes analysis for algae blooms in large river system

    Open Access•Bingfen Cheng, Rui Xia et al.•Sustainable Cities and Society•2019

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