Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images

被引:0
作者
Hu, Wentao [1 ]
Jin, Shuanggen [1 ,2 ,3 ]
Zhang, Yuanyuan [4 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing, Peoples R China
[2] Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China
[3] Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454003, Peoples R China
[4] Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454003, Peoples R China
关键词
Water quality; Rivers; Remote sensing; Data models; Satellites; Accuracy; Monitoring; Reflectivity; Nitrogen; Earth; Genetic algorithm (GA); machine learning; total nitrogen (TN); total phosphorus (TP); Yangtze River; CHLOROPHYLL-A; COASTAL;
D O I
10.1109/JSTARS.2025.3526207
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Total phosphorus (TP) and total nitrogen (TN) are critical water quality indicators in the Yangtze River and remote sensing techniques can inverse these parameters. However, current models suffer from shortcomings such as lower accuracy due to the fewer spectral bands available from a single satellite. In this article, GF-1, Landsat-8, and Sentinel-2 data are jointly used to develop a genetic algorithm-random forest (GA-RF) water quality inversion model weighted by the entropy method. These models are validated and applied to derive long-term time series of TP and TN in the lower Yangtze River from 2018 to 2023. The results indicate that the three-satellite GA-RF joint model shows the best estimation performance from the in-situ measurements: TP with MAE 0.0108 and RMSE 0.0132, and TN with MAE 0.32 and RMSE 0.40. From 2018 to 2023, the water quality shows an improved trend with TP decreasing by 8.91% and TN decreasing by 11.34% . The annual average TP shows a decreasing trend with 0.0017 mg/L per year, while TN shows a decreasing trend with 0.0557 mg/L per year. In terms of seasonal distribution, the highest values of TP and TN are mostly distributed in summer, and the lowest values are mostly distributed in winter. Spatially, both TP and TN increase from west to east. Furthermore, the effects of hydrometeorological factors on water quality are discussed as well as water environmental factors such as pH and NH3-N.
引用
收藏
页码:4992 / 5004
页数:13
相关论文
共 40 条
  • [1] Genetic Algorithm Based on Natural Selection Theory for Optimization Problems
    Albadr, Musatafa Abbas
    Tiun, Sabrina
    Ayob, Masri
    AL-Dhief, Fahad
    [J]. SYMMETRY-BASEL, 2020, 12 (11): : 1 - 31
  • [2] Ali P.J.M., 2014, Mach. Learn Tech. Rep., V1, P1, DOI [10.13140/RG.2.2.28948.04489, DOI 10.13140/RG.2.2.28948.04489]
  • [3] DETECTING WATER-QUALITY PARAMETERS IN THE NORFOLK BROADS, UK, USING LANDSAT IMAGERY
    BABAN, SMJ
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 1993, 14 (07) : 1247 - 1267
  • [4] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [5] Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1007/BF00058655
  • [6] Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data
    Chen, Botao
    Mu, Xi
    Chen, Peng
    Wang, Biao
    Choi, Jaewan
    Park, Honglyun
    Xu, Sheng
    Wu, Yanlan
    Yang, Hui
    [J]. ECOLOGICAL INDICATORS, 2021, 133
  • [7] Remote estimation of colored dissolved organic matter and chlorophyll-a in Lake Huron using Sentinel-2 measurements
    Chen, Jiang
    Zhu, Weining
    Tian, Yong Q.
    Yu, Qian
    Zheng, Yuhan
    Huang, Litong
    [J]. JOURNAL OF APPLIED REMOTE SENSING, 2017, 11
  • [8] Cohen I., 2009, NOISE REDUCTION SPEE, P14, DOI DOI 10.1007/978-3-642-00296-05
  • [9] Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band
    Du, Yun
    Zhang, Yihang
    Ling, Feng
    Wang, Qunming
    Li, Wenbo
    Li, Xiaodong
    [J]. REMOTE SENSING, 2016, 8 (04)
  • [10] Improving prediction of water quality indices using novel hybrid machine -learning algorithms
    Duie Tien Bui
    Khosravi, Khabat
    Tiefenbacher, John
    Nguyen, Hoang
    Kazakis, Nerantzis
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 721