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BGE-ICMER: Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR.

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Title: BGE-ICMER: Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR.
Authors: Pan, Xinyi1 (AUTHOR), Wang, Binhui1,2 (AUTHOR) binhui.wang@helsinki.fi, Wan, Jiahang1,3 (AUTHOR), Song, Shalei3,4 (AUTHOR), Shi, Shuo1,4 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1648. 29p.
Subjects: Reflectance, Spectral reflectance, Plant canopies, Forest monitoring, Calibration, Radiative transfer, Hyperspectral imaging systems
Abstract: Highlights: What are the main findings? An iterative correction method called BGE-ICMER is proposed to mitigate the systematic underestimation of reflectance caused by an unknown effective backscattering cross-section of multi-echo signals. Validation using the three-dimensional radiative transfer model LESS showed that this algorithm reduced the mean relative error of multi-echo reflectance from 26.66% to 10.07%, demonstrating good stability in complex vertical structures with four vegetation layers. What are the implications of the main findings? The BGE-ICMER method eliminates the dependence on specific vegetation single-echo samples, enabling high-precision multi-echo reflectance extraction even in scenarios where pure vegetation echoes are difficult to obtain. By restoring the true physical reflectance in red-edge and near-infrared bands, this study provides a reliable data foundation for subsequent accurate retrieval of vertical profile biochemical parameters such as chlorophyll and equivalent water thickness. Full-waveform hyperspectral LiDAR offers a new approach for precise forest ecological monitoring by simultaneously acquiring the three-dimensional structure and continuous spectral information of targets. However, uncertainty in the backscattering cross-section and the inseparability of the reflectance coefficient lead to systematic underestimation of multi-echo reflectance retrieved using traditional methods. This limitation significantly hinders quantitative applications. The existing multi-echo reflectance correction using neighborhood single-echo reflectance (MCNS) method provides an effective solution by establishing proportional models between similar targets, laying an important foundation for the extraction of multi-echo reflectance. However, its applicability in complex forest scenes is limited due to its dependence on specific vegetation single-echo samples. To address this, an iterative correction method based on ground reflectance baseline, namely Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR (BGE-ICMER), is proposed. Using ground single-echo reflectance as a stable baseline, a multi-target energy distribution model is constructed based on energy conservation, and backscattering cross-section proportions for each echo are iteratively solved to recover true reflectance. Validation using a high-fidelity dataset generated by the Large-Scale remote sensing data and image Simulation framework (LESS) confirmed the effectiveness of the proposed method. This dataset encompasses three typical tree species with vegetation layers ranging from two to four, incorporates micro-topographic ground surfaces and ten spectral channels from 500 to 1000 nm, thereby capturing the structural and spectral complexity of real forests. The results showed that coefficients of determination (R2) between the corrected and true reflectance exceeded 0.9560, with an RMSE below 0.0418 and MAE below 0.0360. The average relative error was reduced from 26.66% to 10.07%, representing a 62.22% improvement in accuracy. Even in the most challenging scenarios with four-layer vegetation occlusion within this dataset, no significant error accumulation occurred. These results demonstrate the robustness and effectiveness of the proposed method for multi-echo reflectance extraction. This study lays a foundation for more accurate forest biochemical attribute assessment and enables the vertical characterization of multiple targets using high-resolution spectral reflectance. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: BGE-ICMER: Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR.
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  Data: Highlights: What are the main findings? An iterative correction method called BGE-ICMER is proposed to mitigate the systematic underestimation of reflectance caused by an unknown effective backscattering cross-section of multi-echo signals. Validation using the three-dimensional radiative transfer model LESS showed that this algorithm reduced the mean relative error of multi-echo reflectance from 26.66% to 10.07%, demonstrating good stability in complex vertical structures with four vegetation layers. What are the implications of the main findings? The BGE-ICMER method eliminates the dependence on specific vegetation single-echo samples, enabling high-precision multi-echo reflectance extraction even in scenarios where pure vegetation echoes are difficult to obtain. By restoring the true physical reflectance in red-edge and near-infrared bands, this study provides a reliable data foundation for subsequent accurate retrieval of vertical profile biochemical parameters such as chlorophyll and equivalent water thickness. Full-waveform hyperspectral LiDAR offers a new approach for precise forest ecological monitoring by simultaneously acquiring the three-dimensional structure and continuous spectral information of targets. However, uncertainty in the backscattering cross-section and the inseparability of the reflectance coefficient lead to systematic underestimation of multi-echo reflectance retrieved using traditional methods. This limitation significantly hinders quantitative applications. The existing multi-echo reflectance correction using neighborhood single-echo reflectance (MCNS) method provides an effective solution by establishing proportional models between similar targets, laying an important foundation for the extraction of multi-echo reflectance. However, its applicability in complex forest scenes is limited due to its dependence on specific vegetation single-echo samples. To address this, an iterative correction method based on ground reflectance baseline, namely Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR (BGE-ICMER), is proposed. Using ground single-echo reflectance as a stable baseline, a multi-target energy distribution model is constructed based on energy conservation, and backscattering cross-section proportions for each echo are iteratively solved to recover true reflectance. Validation using a high-fidelity dataset generated by the Large-Scale remote sensing data and image Simulation framework (LESS) confirmed the effectiveness of the proposed method. This dataset encompasses three typical tree species with vegetation layers ranging from two to four, incorporates micro-topographic ground surfaces and ten spectral channels from 500 to 1000 nm, thereby capturing the structural and spectral complexity of real forests. The results showed that coefficients of determination (R2) between the corrected and true reflectance exceeded 0.9560, with an RMSE below 0.0418 and MAE below 0.0360. The average relative error was reduced from 26.66% to 10.07%, representing a 62.22% improvement in accuracy. Even in the most challenging scenarios with four-layer vegetation occlusion within this dataset, no significant error accumulation occurred. These results demonstrate the robustness and effectiveness of the proposed method for multi-echo reflectance extraction. This study lays a foundation for more accurate forest biochemical attribute assessment and enables the vertical characterization of multiple targets using high-resolution spectral reflectance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3390/rs18101648
    Languages:
      – Code: eng
        Text: English
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        PageCount: 29
        StartPage: 1648
    Subjects:
      – SubjectFull: Reflectance
        Type: general
      – SubjectFull: Spectral reflectance
        Type: general
      – SubjectFull: Plant canopies
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      – SubjectFull: Forest monitoring
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      – SubjectFull: Calibration
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      – SubjectFull: Radiative transfer
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      – SubjectFull: Hyperspectral imaging systems
        Type: general
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      – TitleFull: BGE-ICMER: Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR.
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            NameFull: Pan, Xinyi
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            NameFull: Wang, Binhui
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            NameFull: Wan, Jiahang
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              M: 05
              Text: May2026
              Type: published
              Y: 2026
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