Remote sensing inversion of water quality parameters in the Yellow River Delta

被引:8
|
作者
Cao, Xin [1 ,2 ,3 ]
Zhang, Jing [1 ,2 ,3 ]
Meng, Haobin [1 ,2 ,3 ]
Lai, Yuequn [1 ,2 ,3 ]
Xu, Mofan [3 ]
机构
[1] Capital Normal Univ, Beijing Lab Water Resources Secur, Beijing 100048, Peoples R China
[2] Capital Normal Univ, Key Lab Resource Environm & GIS Beijing, Beijing 100048, Peoples R China
[3] Capital Normal Univ, Coll Resources Environm & Tourism, Beijing 100048, Peoples R China
基金
中国国家自然科学基金;
关键词
Yellow River Delta; Water quality parameters; Remote sensing inversion; Sentinel-2; data; One-dimensional regression; CHLOROPHYLL-A; LAKE;
D O I
10.1016/j.ecolind.2023.110914
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
In recent years, with the rapid socio-economic development of the Yellow River Delta (YRD), the pressure on the supply of water resources has continued to rise. The development of oil-based industries has also led to a series of ecological and environmental problems, such as wetland degradation and water quality deterioration. As an increasing number of rivers are getting polluted, resulting in the deterioration of their water quality, monitoring, managing, and protecting water resources in the YRD is particularly important. In this study, water quality monitoring data and simultaneous Sentinel-2 image data from April 30, 2020, to October 26, 2021, were used to construct an experimental sample in the YRD. Water quality parameters (WQPs) concentrations were correlated with Sentinel-2 image element spectral reflectance and sensitive bands were selected. An empirical method based on the characteristic bands was used to invert a total of six water quality indicators, namely dissolved oxygen (DO), permanganate index (CODMn), ammonia nitrogen (NH3-H), total phosphorus (TP), total nitrogen (TN) and turbidity. The results show: (1) A total of five water quality inversion models for DO, TN, CODMn, TP and TN were effective in the areas of the Guangli River, the Tiaohe and the Branch River. The inversion accuracies of the five inversion models (R2of 0.6099, 0.9271, 0.9581, 0.8784 and 0.7387; RMSE of 1.2723, 0.3413, 0.9923, 0.0118 and 1.8476; RPD of 1.53, 2.08, 3.56, 2.76 and 1.53) indicated the feasibility of the water quality inversion method based on Sentinel-2 data using statistical theory for monitoring water quality concentration in the YRD. (2) The spatial distribution of water quality in the YRD was generally characterized by high water quality in the upper reaches and low water quality in the middle and lower reaches (except for some seasonal variations). Among them, the water quality of the upper reaches of the Guangli River was poor, with opposite trends in DO and TN concentrations. In the Tiaohe, CODMn and TP concentrations were not strongly correlated. However, CODMn and TP concentrations were high in the middle reaches where water quality was the worst. The TN concentrations in the Branch River decreased between 2020 and 2021, but the water quality is still in Category V. Therefore, continued attention and appropriate water quality management measures in the YRD are required. Further, by measuring water quality indicators at monitoring stations, regression-fitting equations for WQPs were established to obtain complementary multi-platform observations. Thus, the water quality conditions in the YRD region can be evaluated more accurately and quickly. The research results not only provide an important reference basis for the identification and monitoring of pollution sources, prevention and treatment of water environment pollution in the YRD, but also provide water security for socio-economic and ecological environment security.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Upscaling remote sensing inversion and dynamic monitoring of soil salinization in the Yellow River Delta, China
    Li, Yinshuai
    Chang, Chunyan
    Wang, Zhuoran
    Zhao, Gengxing
    [J]. ECOLOGICAL INDICATORS, 2023, 148
  • [2] Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong's Rivers
    Zhu, Xi
    Wen, Yansha
    Li, Xiang
    Yan, Feng
    Zhao, Shuhe
    [J]. SUSTAINABILITY, 2023, 15 (08)
  • [3] Dynamic analysis of evapotranspiration based on remote sensing in Yellow River Delta
    Pan Zhiqiang
    Liu Gaohuan
    Zhou Chenghu
    [J]. Journal of Geographical Sciences, 2003, 13 (4) : 408 - 415
  • [5] Using Ensemble Learning for Remote Sensing Inversion of Water Quality Parameters in Poyang Lake
    Peng, Changchun
    Xie, Zhijun
    Jin, Xing
    [J]. SUSTAINABILITY, 2024, 16 (08)
  • [6] Spectral Feature Construction and Sensitivity Analysis of Water Quality Parameters Remote Sensing Inversion
    Wang Xin-hui
    Gong Cai-lan
    Hu Yong
    Li Lan
    He Zhi-jie
    [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41 (06) : 1880 - 1885
  • [7] Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta
    An, Deyu
    Zhao, Gengxing
    Chang, Chunyan
    Wang, Zhuoran
    Li, Ping
    Zhang, Tongrui
    Jia, Jichao
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2016, 37 (02) : 455 - 470
  • [8] Remote sensing monitoring on coastline evolution in the Yellow River Delta since 1976
    Chang, J
    Liu, GH
    Huang, C
    Xu, LR
    [J]. IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium, Vols 1-8, Proceedings, 2005, : 2161 - 2164
  • [9] Application of Remote Sensing Data on Dynamic Monitoring of Coastline in the Yellow River Delta
    Chang, Jun
    Li, Tao
    Liu, Wei
    [J]. ADVANCED MATERIALS, MECHANICS AND INDUSTRIAL ENGINEERING, 2014, 598 : 463 - 466
  • [10] Remote sensing of inland water quality parameters
    Zhang, H
    Zeng, GM
    Huang, GH
    Li, ZW
    Zhao, X
    [J]. ENERGY & ENVIRONMENT - A WORLD OF CHALLENGES AND OPPORTUNITIES, PROCEEDINGS, 2003, : 197 - 201