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Quantitative Inversion Method of Surface Suspended Sand Concentration in Yangtze Estuary Based on Selected Hyperspectral Remote Sensing Bands

Author

Listed:
  • Kuifeng Luan

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Estuarine and Oceanographic Mapping Engineering Research Center of Shanghai, Shanghai 200123, China)

  • Hui Li

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China)

  • Jie Wang

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Estuarine and Oceanographic Mapping Engineering Research Center of Shanghai, Shanghai 200123, China)

  • Chunmei Gao

    (College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, China)

  • Yujia Pan

    (Shanghai Marine Monitoring and Forecasting Center, Shanghai 200062, China)

  • Weidong Zhu

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Estuarine and Oceanographic Mapping Engineering Research Center of Shanghai, Shanghai 200123, China)

  • Hang Xu

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China)

  • Zhenge Qiu

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Estuarine and Oceanographic Mapping Engineering Research Center of Shanghai, Shanghai 200123, China)

  • Cheng Qiu

    (Shanghai Marine Monitoring and Forecasting Center, Shanghai 200062, China)

Abstract

The distribution of the surface suspended sand concentration (SSSC) in the Yangtze River estuary is extremely complex. Therefore, effective methods are needed to improve the efficiency and accuracy of SSSC inversion. Hyperspectral remote sensing technology provides an effective technical means of accurately monitoring and quantitatively inverting SSSC. In this study, a new framework for the accurate inversion of the SSSC in the Yangtze River estuary using hyperspectral remote sensing is proposed. First, we quantitatively simulated water bodies with different SSSCs using sediment samples from the Yangtze River estuary, and analyzed the spectral characteristics of water bodies with different SSSCs. On this basis, we compared six spectral transformation forms, and selected the first derivative (FD) transformation as the optimal spectral transformation form. Subsequently, we compared two feature band extraction methods: the successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) method. Then, the partial least squares regression (PLSR) model and back propagation (BP) neural network model were constructed. The BP neural network model was determined as the best inversion model. The new FD-CARS-BP framework was applied to the airborne hyperspectral data of the Yangtze estuary, with R 2 of 0.9203, RPD of 4.5697, RMSE of 0.0339 kg/m 3 , and RMSE% of 8.55%, which are markedly higher than those of other framework combination forms, further verifying the effectiveness of the FD-CARS-BP framework in the quantitative inversion process of SSSC in the Yangtze estuary.

Suggested Citation

  • Kuifeng Luan & Hui Li & Jie Wang & Chunmei Gao & Yujia Pan & Weidong Zhu & Hang Xu & Zhenge Qiu & Cheng Qiu, 2022. "Quantitative Inversion Method of Surface Suspended Sand Concentration in Yangtze Estuary Based on Selected Hyperspectral Remote Sensing Bands," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13076-:d:940361
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    References listed on IDEAS

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    1. Tinghui Wu & Jian Yu & Jingxia Lu & Xiuguo Zou & Wentian Zhang, 2020. "Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis," Agriculture, MDPI, vol. 10(7), pages 1-14, July.
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