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Using Ensemble Learning for Remote Sensing Inversion of Water Quality Parameters in Poyang Lake

Author

Listed:
  • Changchun Peng

    (Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China)

  • Zhijun Xie

    (Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
    Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo 315211, China)

  • Xing Jin

    (Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China)

Abstract

Inland bodies of water, such as lakes, play a crucial role in sustaining life and supporting ecosystems. However, with the rapid development of socio-economics, water resources are facing serious pollution problems, such as the eutrophication of water bodies and degradation of wetlands. Therefore, the monitoring, management, and protection of inland water resources are particularly important. In past research, empirical models and machine learning models have been widely used for the water quality assessment of inland lakes. Due to the complexity of the optical properties of inland lake water bodies, the performance of these models is often limited. To overcome the limitations of these models, this study uses in situ water quality data from 2017 to 2018 and multispectral (MS) remote sensing data from Sentinel-2 to construct experimental samples of Poyang Lake. Based on these experimental samples, we constructed a spatio-temporal ensemble model (STE) to evaluate four common water quality parameters: chlorophyll-a (Chl-a), total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). The model adopts an ensemble learning strategy, improving the model’s performance by merging multiple advanced machine learning algorithms. We introduced several indices related to water quality parameters as auxiliary variables, such as NDCI and Enhanced Three, and used band data and these auxiliary variables as predictive variables, thereby greatly enhancing the predictive potential of the model.The results show that the inversion accuracy of these four inversion models is high ( R 2 of 0.94, 0.88, 0.92, and 0.93; RMSE of 1.15, 0.01, 0.02, and 0.02; MAE of 0.81, 0.01, 0.09, and 0.10), indicating that the STE model has good evaluation accuracy. Meanwhile, we used the STE model to reveal the spatio-temporal distribution of Chl-a, TP, TN, and COD from 2017 to 2018, and analyzed their seasonal and spatial variation rules. The results of this study not only provide an effective and practical method for monitoring and managing water quality parameters in inland lakes, but also provide water security for socio-economic and ecological environmental safety.

Suggested Citation

  • Changchun Peng & Zhijun Xie & Xing Jin, 2024. "Using Ensemble Learning for Remote Sensing Inversion of Water Quality Parameters in Poyang Lake," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3355-:d:1377197
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    References listed on IDEAS

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    1. Fernanda Sayuri Yoshino Watanabe & Enner Alcântara & Thanan Walesza Pequeno Rodrigues & Nilton Nobuhiro Imai & Cláudio Clemente Faria Barbosa & Luiz Henrique da Silva Rotta, 2015. "Estimation of Chlorophyll-a Concentration and the Trophic State of the Barra Bonita Hydroelectric Reservoir Using OLI/Landsat-8 Images," IJERPH, MDPI, vol. 12(9), pages 1-27, August.
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    3. Jeff C. Ho & Anna M. Michalak & Nima Pahlevan, 2019. "Widespread global increase in intense lake phytoplankton blooms since the 1980s," Nature, Nature, vol. 574(7780), pages 667-670, October.
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