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The phenology of European forests as seen by MODIS Leaf Area Index and GEDI Plant Area Index: Toward an integrated approach.

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Title: The phenology of European forests as seen by MODIS Leaf Area Index and GEDI Plant Area Index: Toward an integrated approach.
Authors: Cotrina-Sanchez, Alexander1,2 (AUTHOR), Coomes, David A.2 (AUTHOR), Ball, James2 (AUTHOR), Holcomb, Amelia3 (AUTHOR), Valentini, Riccardo1 (AUTHOR), Vaglio Laurin, Gaia1,4 (AUTHOR) gaia.vagliolaurin@cnr.it
Source: Forest Ecology & Management. Jul2026, Vol. 611, pN.PAG-N.PAG. 1p.
Subject Terms: *Temperate forests, *Phenology, *Climate change, *Plant canopies, Leaf area index, Remote sensing, Seasonal physiological variations
Company/Entity: Earth Observing System (Program)
Abstract: The timing of phenological events, such as the start of season (SOS) and end of the season (EOS), is critical to understand the response of terrestrial ecosystems to climate change. Phenology patterns in space are not easily detected in multi-layered canopy structures, such as broadleaved deciduous forests; discrepancies in measures from the ground and space are known. Lidar signals can penetrate canopy and is potentially useful to solve some of the challenges in remote sensing phenology. Here, phenology time series derived from LiDAR-based Plant Area Index (PAI) from the Global Ecosystem Dynamics Investigation (GEDI) were compared with passive optical Leaf Area Index (LAI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and further evaluated using high-resolution Sentinel-2–derived phenological metrics Results evidence clear differences in the detection of the senescence phase in broadleaved European forests at different latitudes, with GEDI-PAI estimating EOS up to 49 days later than MODIS-LAI. Sentinel-2–derived EOS dates were intermediate between MODIS and GEDI estimates (∼35 days), supporting the interpretation that GEDI captures structural signals persisting beyond optical senescence. GEDI-PAI consistently retrieved later EOS dates and longer growing season length, reflecting its sensitivity to canopy structural changes during leaf fall. Robust phenological signals were detected in broadleaved forests, whereas needleleaved forests showed limited seasonal GEDI-PAI variability. In contrast, MODIS-LAI better captures changes in leaf color and greenness and better represents fine-scale variations during the active growing season. Overall, these findings demonstrate that optical and spaceborne LiDAR sensors capture complementary aspects of forest phenology, and that their integration improves phenological characterization relevant to ecological and climate change research. • GEDI-PAI has the capability to detect seasonal variations in European forests. • GEDI-PAI and MODIS-LAI showed a high correlation in broadleaved forests. • MODIS-LAI values are higher than GEDI-PAI during the active growing period. • In contrast to MODIS-LAI, GEDI-PAI detects the senescence phase later in broadleaved forests. • GEDI-PAI_z captures foliage changes in vegetative/non-vegetative periods along vertical profile. [ABSTRACT FROM AUTHOR]
Copyright of Forest Ecology & Management is the property of Elsevier B.V. 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: The phenology of European forests as seen by MODIS Leaf Area Index and GEDI Plant Area Index: Toward an integrated approach.
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  Label: Abstract
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  Data: The timing of phenological events, such as the start of season (SOS) and end of the season (EOS), is critical to understand the response of terrestrial ecosystems to climate change. Phenology patterns in space are not easily detected in multi-layered canopy structures, such as broadleaved deciduous forests; discrepancies in measures from the ground and space are known. Lidar signals can penetrate canopy and is potentially useful to solve some of the challenges in remote sensing phenology. Here, phenology time series derived from LiDAR-based Plant Area Index (PAI) from the Global Ecosystem Dynamics Investigation (GEDI) were compared with passive optical Leaf Area Index (LAI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and further evaluated using high-resolution Sentinel-2–derived phenological metrics Results evidence clear differences in the detection of the senescence phase in broadleaved European forests at different latitudes, with GEDI-PAI estimating EOS up to 49 days later than MODIS-LAI. Sentinel-2–derived EOS dates were intermediate between MODIS and GEDI estimates (∼35 days), supporting the interpretation that GEDI captures structural signals persisting beyond optical senescence. GEDI-PAI consistently retrieved later EOS dates and longer growing season length, reflecting its sensitivity to canopy structural changes during leaf fall. Robust phenological signals were detected in broadleaved forests, whereas needleleaved forests showed limited seasonal GEDI-PAI variability. In contrast, MODIS-LAI better captures changes in leaf color and greenness and better represents fine-scale variations during the active growing season. Overall, these findings demonstrate that optical and spaceborne LiDAR sensors capture complementary aspects of forest phenology, and that their integration improves phenological characterization relevant to ecological and climate change research. • GEDI-PAI has the capability to detect seasonal variations in European forests. • GEDI-PAI and MODIS-LAI showed a high correlation in broadleaved forests. • MODIS-LAI values are higher than GEDI-PAI during the active growing period. • In contrast to MODIS-LAI, GEDI-PAI detects the senescence phase later in broadleaved forests. • GEDI-PAI_z captures foliage changes in vegetative/non-vegetative periods along vertical profile. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Forest Ecology & Management is the property of Elsevier B.V. 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:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.foreco.2026.123689
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Temperate forests
        Type: general
      – SubjectFull: Phenology
        Type: general
      – SubjectFull: Climate change
        Type: general
      – SubjectFull: Plant canopies
        Type: general
      – SubjectFull: Leaf area index
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Seasonal physiological variations
        Type: general
      – SubjectFull: Earth Observing System (Program)
        Type: general
    Titles:
      – TitleFull: The phenology of European forests as seen by MODIS Leaf Area Index and GEDI Plant Area Index: Toward an integrated approach.
        Type: main
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          Name:
            NameFull: Cotrina-Sanchez, Alexander
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            NameFull: Coomes, David A.
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            NameFull: Ball, James
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            NameFull: Holcomb, Amelia
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            NameFull: Valentini, Riccardo
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            NameFull: Vaglio Laurin, Gaia
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          Dates:
            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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              Value: 611
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            – TitleFull: Forest Ecology & Management
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