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    Researchers improved land surface modeling by adding a forward scattering kernel function to the traditional linear kernel-driven model. This enhanced model accurately fits snow/ice bidirectional reflectance factor (BRF) data.

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    Area of Science:

    • Remote Sensing
    • Geophysics
    • Optical Physics

    Background:

    • Accurate modeling of land surface reflectance anisotropy is crucial for remote sensing.
    • Traditional kernel-driven models, based on vegetation canopies, inadequately represent snow/ice forward scattering.
    • Existing empirical functions lack the precision for snow/ice surface BRF fitting.

    Purpose of the Study:

    • To develop an improved linear kernel-driven model for land surface reflectance anisotropy.
    • To incorporate a forward scattering kernel function to better represent snow/ice surfaces.
    • To validate the enhanced model's performance using in situ measured bidirectional reflectance factor (BRF) data.

    Main Methods:

    • Proposed a novel linear kernel-driven model by integrating a forward kernel function.
    • Validated the model using in situ measured bidirectional reflectance factor (BRF) data.
    • Compared the proposed model's performance against empirical functions and the traditional linear kernel-driven model.

    Main Results:

    • The improved model demonstrated high efficiency in fitting snow/ice BRF data, achieving R²=0.9975 and RMSE=0.0226.
    • The addition of the forward kernel function significantly enhanced fitting accuracy compared to the traditional model.
    • The enhanced model exhibited stable fitting results across different wavelengths, indicating robustness.

    Conclusions:

    • The modified linear kernel-driven model with a forward kernel function is highly effective for modeling snow/ice surface reflectance anisotropy.
    • This approach offers a significant improvement over traditional methods and empirical functions for snow/ice remote sensing applications.
    • The model's stability across wavelengths underscores its reliability for diverse remote sensing scenarios involving snow and ice.