Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance

Helsen, Kenny; Bassi, Leonardo; Feilhauer, Hannes; Kattenborn, Teja; Matsushima, Hajime; Cleemput, Elisa Van; Somers, Ben; Honnay, Olivier

doi:10.1016/j.ecolind.2021.108111

Cited by 11 publications

(6 citation statements)

References 61 publications

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“…6; Table S5). This finding is consistent with previous studies transferring models among species (Helsen et al 2021) or sites (Yan et al 2021). In general, trait models performed worse when the trait distributions in the calibration and validation datasets were less similar, but the strength and even the direction of the trend varied among traits (Fig.…”

Section: Discussionsupporting

confidence: 93%

Predicting leaf traits across functional groups using reflectance spectroscopy

Kothari

Beauchamp‐Rioux

Blanchard

et al. 2022

Preprint

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Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2 = 0.55-0.85; %RMSE = 12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

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Section: Discussionsupporting

confidence: 93%

Predicting leaf traits across functional groups using reflectance spectroscopy

Kothari

Beauchamp‐Rioux

Blanchard

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…6; Table S5). This finding is consistent with previous studies transferring models among species (Helsen et al ., 2021) or sites (Yan et al ., 2021). In general, trait models performed worse when the trait distributions in the calibration and validation datasets were less similar, but the strength and even the direction of the trend varied among traits (Fig.…”

Section: Discussionmentioning

confidence: 99%

Predicting leaf traits across functional groups using reflectance spectroscopy

et al. 2023

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Summary Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non‐destructive estimates of leaf traits, but it remains unclear whether general trait‐spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least‐squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2 = 0.55–0.85; %RMSE = 12.7–19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

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“…4) and PFTs (Fig. 7), agrees with several other recent studies of PLSR transferability across species (Helsen et al ., 2021), across sites (Nakaji et al ., 2019; Yan et al ., 2021). As well as limited PLSR transferability through time (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…One such study (Serbin et al ., 2019c) shows that a single PLSR model achieves good accuracy for LMA estimates across a wide phylogenetic, geographic, and climatic range (woody and herbaceous species in the Americas over a range spanning Arctic Alaska to the Colombian Amazon). Instead, a study of four species typical of coastal sand dune grasslands along the Belgian North Sea coast (Helsen et al ., 2021) found significantly lower accuracy for across‐species PLSR models than species‐specific PLSR models for retrieving LMA. Another study (Heckmann et al ., 2017) investigated the transferability of models between the Brassicaceae genera Moricandia and Brassica .…”

Section: Introductionmentioning

confidence: 97%

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

Ji,

Li,

Hao

et al. 2024

New Phytologist

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Summary Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross‐validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

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Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance

Cited by 11 publications

References 61 publications

Predicting leaf traits across functional groups using reflectance spectroscopy

Predicting leaf traits across functional groups using reflectance spectroscopy

Predicting leaf traits across functional groups using reflectance spectroscopy

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

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