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River algal blooms can be estimated by remote sensing reflectance

文献类型：期刊论文

作者Huang, Tonghui2,3; Xia, Rui3; Zhang, Kai3; Chen, Yan3; Ren, Yuanxin2; Song, Jinxi2; Wang, Yao3; Liu, Chengjian1
刊名ENVIRONMENTAL RESEARCH LETTERS
出版日期2024-10-01
卷号19期号:10页码:13
关键词algal blooms remote sensing inversion genetic algorithm and regression tree response threshold the Han River of China
ISSN号1748-9326
DOI10.1088/1748-9326/ad7043
产权排序3
英文摘要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 x 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 x 107 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.
WOS关键词ESTIMATING CHLOROPHYLL-A ; WATER-QUALITY ; SEMIANALYTICAL MODEL ; RETRIEVAL ALGORITHMS ; TAIHU LAKE ; PREDICTION ; RESERVOIR ; TM
资助项目National Key R&D Program of China ; National Natural Science Foundation of China[52479078] ; Joint Research Program for Ecological Conservation and High Quality Development of the Yellow River Basin[2022-YRUC-01] ; [2021YFC3201003]
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001313949500001
出版者IOP Publishing Ltd
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Joint Research Program for Ecological Conservation and High Quality Development of the Yellow River Basin
源URL[http://ir.igsnrr.ac.cn/handle/311030/208415]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Xia, Rui; Zhang, Kai
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
2.Northwest Univ, Coll Urban & Environm Sci, Shaanxi Key Lab Earth Surface Syst & Environm Carr, Xian 710127, Peoples R China
3.Chinese Res Inst Environm Sci, State Key Lab Environm Criteria & Risk Assessment, Beijing 100012, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Tonghui,Xia, Rui,Zhang, Kai,et al. River algal blooms can be estimated by remote sensing reflectance[J]. ENVIRONMENTAL RESEARCH LETTERS,2024,19(10):13.
APA Huang, Tonghui.,Xia, Rui.,Zhang, Kai.,Chen, Yan.,Ren, Yuanxin.,...&Liu, Chengjian.(2024).River algal blooms can be estimated by remote sensing reflectance.ENVIRONMENTAL RESEARCH LETTERS,19(10),13.
MLA Huang, Tonghui,et al."River algal blooms can be estimated by remote sensing reflectance".ENVIRONMENTAL RESEARCH LETTERS 19.10(2024):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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