Retrieval of water quality parameters by hyperspectral remote sensing in lake TaiHu, China

被引:2
|
作者
Yang, DT [1 ]
Pan, DL [1 ]
Zhang, XY [1 ]
Zhang, XF [1 ]
He, XQ [1 ]
Li, SQ [1 ]
机构
[1] SOA, Inst Oceanog 2, Lab Ocean Dynam Proc & Satellite Oceanog, Hangzhou 310012, Peoples R China
关键词
hyperspectral remote sensing; water quality; TaiHu;
D O I
10.1117/12.619673
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Monitoring and restoration the water quality of lake need proper water quality parameters. Traditional measurement of water quality, requiring laborious laboratory work, is expensive and time consuming. Hyperspectral measurement can offer fast and easy way for estimating trophic status. Hyperspectral data on 7-8, March, 2004 and water chemical data from 1997 to 2003 was used for retrieval of water quality parameters. The quantification of spectra with water quality parameters: chlorophyll a, suspended solids, total nitrogen(TN), total phosphorus(TP), chemical oxygen demand(COD), secchi depth(SD) were regressed. Results showed that the reflectance ratio of R702/R685, R620/R531 and R554/R675 had high correlations with the concentration of chlorophyll a, suspended solids and total phosphorus respectively; TN, COD can be calculated from TP or Chl a for good relations between them; SD is negatively correlated with suspended solids concentration, total phosphorus (TP) (less than 0.25 mg/L) linearly correlated with logarithm chlorophyll a concentration; trophic status index (TSI) exponentially correlated with COD concentration.
引用
收藏
页码:431 / 439
页数:9
相关论文
共 50 条
  • [21] Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing
    Cui, Mengying
    Sun, Yonghua
    Huang, Chen
    Li, Mengjun
    [J]. WATER, 2022, 14 (01)
  • [22] Identification for the species of aquatic higher plants in the Taihu Lake basin based on hyperspectral remote sensing
    Mu, Shichen
    You, Kai
    Song, Ting
    Li, Yajie
    Wang, Lihong
    Shi, Junzhe
    [J]. ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (08)
  • [23] Identification for the species of aquatic higher plants in the Taihu Lake basin based on hyperspectral remote sensing
    Shichen Mu
    Kai You
    Ting Song
    Yajie Li
    Lihong Wang
    Junzhe Shi
    [J]. Environmental Monitoring and Assessment, 2023, 195
  • [24] Effects on water quality following water transfer in Lake Taihu, China
    Hu, Liuming
    Hu, Weiping
    Zhai, Shuhua
    Wu, Haoyun
    [J]. ECOLOGICAL ENGINEERING, 2010, 36 (04) : 471 - 481
  • [25] Application of airborne hyperspectral remote sensing for the retrieval of forest inventory parameters
    Dmitriev, Yegor V.
    Kozoderov, Vladimir V.
    Sokolov, Anton A.
    [J]. MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL REMOTE SENSING TECHNOLOGY, TECHNIQUES AND APPLICATIONS VI, 2016, 9880
  • [26] Advance in remote sensing of lake water quality
    Zhang, Bo
    Zhang, Bai
    Hong, Mei
    Duan, Hong-Tao
    Song, Kai-Shan
    Wang, Zong-Ming
    [J]. Shuikexue Jinzhan/Advances in Water Science, 2007, 18 (02): : 301 - 310
  • [27] Characteristic Wavelengths Analysis for Remote Sensing Reflectance on Water Surface in Taihu Lake
    Shen Qian
    Zhang Bing
    Li Jun-sheng
    Wu Yuan-feng
    Wu Di
    Song Yang
    Zhang Fang-fang
    Wang Gan-lin
    [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31 (07) : 1892 - 1897
  • [28] Water quality assessment using hyperspectral remote sensing
    Wiangwang, N
    Qi, JG
    [J]. IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium, Vols 1-8, Proceedings, 2005, : 4531 - 4534
  • [29] Water quality model using hyperspectral remote sensing
    Lopes, Fernando B.
    Barbosa, Claudio C. F.
    Novo, Evlyn M. L. de M.
    de Andrade, Eunice M.
    Chaves, Luiz C. G.
    [J]. REVISTA BRASILEIRA DE ENGENHARIA AGRICOLA E AMBIENTAL, 2014, 18 : S13 - S19
  • [30] Retrieval Model for Water Quality Parameters of Miyun Reservoir Based on UAV Hyperspectral Remote Sensing Data and Deep Neural Network Algorithm
    Qiao Zhi
    Jiang Qun-ou
    Lu Ke-xin
    Gao Feng
    [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44 (07) : 2066 - 2074