Research progress in surface water quality monitoring based on remote sensing technology

被引:1
作者
Zheng, Yue [1 ]
Wang, Jianjun [2 ]
Kondratenko, Yuriy [3 ]
Wu, Junhong [4 ]
机构
[1] Yancheng Polytech Coll, Dept Sci & Technol, Yancheng 224005, Peoples R China
[2] Yunzhou Yancheng Innovat Technol Co Ltd, Dept Res & Develpoment, Yancheng, Peoples R China
[3] Petro Mohyla Black Sea Natl Univ, Dept Intelligent Control Syst, Nikolayev, Ukraine
[4] Jiangsu Xingyue Surveying & Mapping Technol Co Lt, Res & Dev Ctr Engn Technol, Yancheng, Peoples R China
关键词
Remote sensing; water quality parameters; inverse model; hyperspectral data; TOTAL SUSPENDED MATTER; CHLOROPHYLL-A; SPATIOTEMPORAL VARIATIONS; ATMOSPHERIC CORRECTION; RIVER NETWORK; INLAND WATERS; LANDSAT; 8; COASTAL; RETRIEVAL; MODEL;
D O I
10.1080/01431161.2024.2327086
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Urban surface water is an important freshwater resource, and the surface water environment is increasingly being destroyed. Dynamic monitoring of surface water is of great significance for protecting the ecological environment. Remote sensing technology provides technical support for surface water monitoring, which overcomes the drawbacks of traditional manual sampling. It has been widely applied in surface water monitoring. The paper systematically reviews the research progress of remote sensing technology in surface water monitoring from the aspects of remote sensing data, inversion models and water quality parameters. Advantages and disadvantages of inversion models (analytical methods, empirical methods, semi-empirical methods, machine learning methods and comprehensive methods) are compared and analysed. Furthermore, we summarize the research progress of remote sensing technology in monitoring chlorophyll a (Chl-a), total suspended matter (TSM), coloured dissolved organic matter (CDOM), transparency and non-photosensitive parameters. Although remote sensing technology provides new ideas for surface water monitoring, there are still some problems that need to be solved, such as remote sensing signals being affected by the atmosphere, poor portability of inversion models, low resolution of satellite sensors, and susceptibility to external factors. Therefore, future research should combine multi-source data, conduct in-depth research on the optical characteristics of surface water bodies, optimize inversion methods, construct transferable inversion models, break through temporal and spatial limitations, and promote the rapid development of surface water pollution monitoring and warning.
引用
收藏
页码:2337 / 2373
页数:37
相关论文
共 195 条
  • [1] Abayazid Hala O., 2019, Journal of Water Resource and Protection, V11, P713, DOI 10.4236/jwarp.2019.116042
  • [2] A Bio-optical Numerical Approach for Remote Retrieval of Total Suspended Matter from Turbid Waters
    Adhikari, Arjun
    Menon, Harilal B.
    [J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2022, 50 (09) : 1773 - 1786
  • [3] Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques
    Adjovu, Godson Ebenezer
    Stephen, Haroon
    James, David
    Ahmad, Sajjad
    [J]. REMOTE SENSING, 2023, 15 (14)
  • [4] Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters
    Adjovu, Godson Ebenezer
    Stephen, Haroon
    James, David
    Ahmad, Sajjad
    [J]. REMOTE SENSING, 2023, 15 (07)
  • [5] An Enhanced Water Quality Index for Water Quality Monitoring Using Remote Sensing and Machine Learning
    Ahmed, Mehreen
    Mumtaz, Rafia
    Anwar, Zahid
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (24):
  • [6] Development of Remote Sensing Based Models for Surface Water Quality
    Akbar, Tahir Ali
    Hassan, Quazi K.
    Achari, Gopal
    [J]. CLEAN-SOIL AIR WATER, 2014, 42 (08) : 1044 - 1051
  • [7] Sea water chlorophyll-a estimation using hyperspectral images and supervised Artificial Neural Network
    Awad, Mohamad
    [J]. ECOLOGICAL INFORMATICS, 2014, 24 : 60 - 68
  • [8] Remote sensing-based water quality monitoring in African reservoirs, potential and limitations of sensors and algorithms: A systematic review
    Bangira, Tsitsi
    Matongera, Trylee Nyasha
    Mabhaudhi, Tafadzwanashe
    Mutanga, Onisimo
    [J]. PHYSICS AND CHEMISTRY OF THE EARTH, 2024, 134
  • [9] Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data
    Bannari, Abderrazak
    Staenz, Karl
    Champagne, Catherine
    Khurshid, K. Shahid
    [J]. REMOTE SENSING, 2015, 7 (06) : 8107 - 8127
  • [10] Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data
    Belen Ruescas, Ana
    Hieronymi, Martin
    Mateo-Garcia, Gonzalo
    Koponen, Sampsa
    Kallio, Kari
    Camps-Valls, Gustau
    [J]. REMOTE SENSING, 2018, 10 (05):