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A Terrain-Corrected Vegetation Index Strategy for Improving Leaf Area Index Estimation in Mountainous Areas.

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Title: A Terrain-Corrected Vegetation Index Strategy for Improving Leaf Area Index Estimation in Mountainous Areas.
Authors: Liu, Haier1 (AUTHOR), Hu, Guyue1,2 (AUTHOR) hugy@cdut.edu.cn, Liu, Chenghao3 (AUTHOR), Han, Yakun1 (AUTHOR), Li, Siqi1,2 (AUTHOR), Yang, Ronghao1,3 (AUTHOR), Tan, Junxiang1 (AUTHOR), Li, Shaoda1 (AUTHOR)
Source: Remote Sensing. Sep2026, Vol. 18 Issue 17, p2977. 22p.
Subjects: Leaf area index, Normalized difference vegetation index, Environmental monitoring, Random forest algorithms, Altitudes, Remote sensing, Alpine regions, Topography
Abstract: Highlights: What are the main findings? A LAI retrieval method suitable for mountainous areas integrating terrain-corrected vegetation indices and Random Forest Regression was developed. The NDVISCSC-based LAI retrieval method performed optimally (with R2 and RMSE of 0.927 and 0.151, respectively) under various terrain conditions. What are the implications of the main findings? Terrain effects can introduce significant uncertainties in VI-based LAI retrieval in mountainous areas. The proposed LAI retrieval method and the terrain-corrected vegetation indices can provide an effective tool for surface biophysical parameter extraction in mountainous areas. Leaf Area Index (LAI) is an important biophysical parameter in studies of regional and global ecosystems. However, terrain-induced distortion of surface reflectance can reduce the reliability of vegetation indices (VIs) in characterizing the canopy structure, thereby introducing uncertainties in LAI retrieval. In this study, an LAI retrieval method for mountainous areas based on the combination of terrain-corrected VIs and the random forest algorithm was proposed. Typical topographic correction models (Cosine+C, SCS+C, and Statistical–Empirical) were applied to normalize surface reflectance, and the terrain-corrected normalized difference vegetation index (NDVI) and modified soil-adjusted vegetation index (MSAVI) were constructed accordingly. Then, random forest regression was used for LAI retrieval, and the proposed method was validated through comparisons of this LAI with the field observations and original VI-based methods. The results showed that topographic correction effectively reduced the radiometric distortions induced by topography, and the NDVISCSC-based retrieval method performed well under various terrain conditions (with R2 and RMSE of 0.927 and 0.151, respectively). In addition, to investigate the effects of different terrain factors and illumination conditions on LAI retrieval, methods based on the original and terrain-corrected VIs were compared for surfaces with different slopes and aspects. The results revealed that the terrain-corrected VIs can improve the performance of LAI retrieval in areas with terrain-induced reflectance distortion. Finally, the optimal method successfully estimated LAI in the study area. Therefore, the proposed LAI retrieval method for mountainous areas is an effective tool for extracting surface biophysical parameters, and can provide a reliable approach for regional ecological monitoring and evaluation. [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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  Label: Title
  Group: Ti
  Data: A Terrain-Corrected Vegetation Index Strategy for Improving Leaf Area Index Estimation in Mountainous Areas.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Haier%22">Liu, Haier</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Guyue%22">Hu, Guyue</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> hugy@cdut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Chenghao%22">Liu, Chenghao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Yakun%22">Han, Yakun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Siqi%22">Li, Siqi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Ronghao%22">Yang, Ronghao</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tan%2C+Junxiang%22">Tan, Junxiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Shaoda%22">Li, Shaoda</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Sep2026, Vol. 18 Issue 17, p2977. 22p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Leaf+area+index%22">Leaf area index</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Altitudes%22">Altitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Alpine+regions%22">Alpine regions</searchLink><br /><searchLink fieldCode="DE" term="%22Topography%22">Topography</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? A LAI retrieval method suitable for mountainous areas integrating terrain-corrected vegetation indices and Random Forest Regression was developed. The NDVISCSC-based LAI retrieval method performed optimally (with R2 and RMSE of 0.927 and 0.151, respectively) under various terrain conditions. What are the implications of the main findings? Terrain effects can introduce significant uncertainties in VI-based LAI retrieval in mountainous areas. The proposed LAI retrieval method and the terrain-corrected vegetation indices can provide an effective tool for surface biophysical parameter extraction in mountainous areas. Leaf Area Index (LAI) is an important biophysical parameter in studies of regional and global ecosystems. However, terrain-induced distortion of surface reflectance can reduce the reliability of vegetation indices (VIs) in characterizing the canopy structure, thereby introducing uncertainties in LAI retrieval. In this study, an LAI retrieval method for mountainous areas based on the combination of terrain-corrected VIs and the random forest algorithm was proposed. Typical topographic correction models (Cosine+C, SCS+C, and Statistical–Empirical) were applied to normalize surface reflectance, and the terrain-corrected normalized difference vegetation index (NDVI) and modified soil-adjusted vegetation index (MSAVI) were constructed accordingly. Then, random forest regression was used for LAI retrieval, and the proposed method was validated through comparisons of this LAI with the field observations and original VI-based methods. The results showed that topographic correction effectively reduced the radiometric distortions induced by topography, and the NDVISCSC-based retrieval method performed well under various terrain conditions (with R2 and RMSE of 0.927 and 0.151, respectively). In addition, to investigate the effects of different terrain factors and illumination conditions on LAI retrieval, methods based on the original and terrain-corrected VIs were compared for surfaces with different slopes and aspects. The results revealed that the terrain-corrected VIs can improve the performance of LAI retrieval in areas with terrain-induced reflectance distortion. Finally, the optimal method successfully estimated LAI in the study area. Therefore, the proposed LAI retrieval method for mountainous areas is an effective tool for extracting surface biophysical parameters, and can provide a reliable approach for regional ecological monitoring and evaluation. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  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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    Identifiers:
      – Type: doi
        Value: 10.3390/rs18172977
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 2977
    Subjects:
      – SubjectFull: Leaf area index
        Type: general
      – SubjectFull: Normalized difference vegetation index
        Type: general
      – SubjectFull: Environmental monitoring
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Altitudes
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Alpine regions
        Type: general
      – SubjectFull: Topography
        Type: general
    Titles:
      – TitleFull: A Terrain-Corrected Vegetation Index Strategy for Improving Leaf Area Index Estimation in Mountainous Areas.
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            NameFull: Liu, Haier
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            – D: 01
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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            – TitleFull: Remote Sensing
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