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Satellite-derived bathymetry based on machine learning models and an updated quasi-analytical algorithm approach.

Zhongqiang Wu, Zhihua Mao, Wei Shen

    Optics Express
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    Summary

    Satellite-derived bathymetry is enhanced by incorporating water quality parameters from the updated quasi analysis algorithm (UQAA). This method improves accuracy in shallow waters compared to using only remote sensing reflectance, aiding underwater terrain mapping.

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    Area of Science:

    • Oceanography
    • Remote Sensing
    • Geophysics

    Background:

    • Satellite-derived bathymetry is crucial for mapping underwater terrain in shallow optical waters.
    • Bottom reflectance significantly influences water-leaving radiance, impacting depth retrieval accuracy.
    • The updated quasi analysis algorithm (UQAA) derives water quality parameters correlated with bottom brightness and depth.

    Purpose of the Study:

    • To evaluate the influence of UQAA-derived inherent optical properties (IOPs) on bathymetry retrieval.
    • To compare the performance of various machine learning algorithms for satellite-derived bathymetry using UQAA.
    • To assess the accuracy improvement in water depth estimation by integrating UQAA parameters.

    Main Methods:

    • Utilized WorldView-2 multispectral imagery and laser measurement data for Ganquan Island, South China Sea.
    • Applied machine learning models including Backpropagation (BP) neural network, Extreme Learning Machine (ELM), Random Forest (RF), Adaboost, and Support Vector Regression (SVR).
    • Evaluated remote sensing reflectance at in-situ depth points and detection accuracy with and without UQAA parameters.

    Main Results:

    • Bathymetry retrieval integrating UQAA parameters and remote sensing reflectance outperformed methods using only remote sensing reflectance.
    • Root Mean Square Error (RMSE) improved by 1-5 cm, and Mean Relative Error (MRE) improved by 1-5%.
    • UQAA results demonstrated significant potential as a key eigenvalue for enhancing water depth estimation accuracy.

    Conclusions:

    • The integration of UQAA-derived water quality parameters significantly improves satellite-derived bathymetry accuracy in shallow optical waters.
    • Machine learning algorithms effectively utilize UQAA parameters for more precise depth estimation.
    • This approach offers a more robust and accurate method for mapping underwater topography via remote sensing.