Spectral subdomains and prior estimation of leaf structure improves PROSPECT inversion on reflectance or transmittance alone

Spafford, Lynsay; Maire, Guerric Le; MacDougall, Andrew H.; Boissieu, Florian de; Féret, Jean‐Baptiste

doi:10.1016/j.rse.2020.112176

Cited by 41 publications

(27 citation statements)

References 85 publications

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“…It may be relevant that the traits that show the greatest improvement include most of the highly area‐proportional traits. Given that transmittance or absorptance spectra yielded little improvement for most traits, adding transmittance measurements may not aid much in trait estimation, although they are still useful in endeavors like radiative transfer modeling (Spafford et al ., 2021).…”

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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Section: Discussionmentioning

confidence: 99%

Predicting leaf traits across functional groups using reflectance spectroscopy

et al. 2023

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“…Scaling leaf traits measured in situ up to the canopy level requires particular care as canopy reflectance is influenced by many factors (Roelofsen et al, 2013; Feilhauer et al, 2017). Furthermore, some traits integrated in the RTM models, and proposed as practical explanations to otherwise complex problems (e.g., brown pigments and N in PROSPECT; Jacquemoud & Baret, 1990; Feilhauer et al, 2017; Spafford et al, 2021), cannot be measured easily experimentally. This makes the validation of some retrieved optical traits challenging.…”

Section: Introductionmentioning

confidence: 99%

Which optical traits enable an estimation of tree species diversity based on the Spectral Variation Hypothesis?

Torresani

Feilhauer

Rocchini

et al. 2021

Applied Vegetation Science

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Question Which optical traits, retrieved from biophysical models applied to Sentinel‐2 images, enable an estimation of tree species diversity based on the Spectral Variation Hypothesis? Location Coniferous mountain forest in the eastern Italian Alps. Methods We analyzed the PROSPECT‐5 and Invertible Forest Reflectance Model (INFORM) biophysical parameters as retrieved from canopy reflectance data of different forest plots (using Sentinel‐2 images for the years 2017, 2018 and 2019) as optical trait indicators (OTIs). We successively tested the Spectral Variation Hypothesis (SVH) for each retrieved OTI using the Rao's Q as heterogeneity index, validating them against Shannon's H values calculated as a tree species diversity index derived from in‐situ collected data. Results We demonstrated differences among OTIs in terms of how well their variations can be linked to species diversity. In particular the variations of brown pigments (Cbrown), carotenoids (Car) and chlorophyll content (Cab) can be considered the most relevant OTIs for the application of the SVH when using the Rao's Q as a proxy for tree species diversity in our study site. Conclusions This research underlined that the OTIs contribute differently in the SVH to estimate tree species diversity, highlighting significant positive correlations between tree species diversity and the spatial heterogeneity of the estimated pigment content (Cab, Car, Cbrown).

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“…It may be relevant that the traits that show the greatest improvement include most of the highly area-proportional traits. Given that transmittance or absorptance spectra yielded little improvement for most traits, adding transmittance measurements may not aid much in trait estimation, although they are still useful in endeavors like radiative transfer modeling (Spafford et al 2021).…”

Section: Discussionmentioning

confidence: 99%

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.

show abstract

Spectral subdomains and prior estimation of leaf structure improves PROSPECT inversion on reflectance or transmittance alone

Cited by 41 publications

References 85 publications

Predicting leaf traits across functional groups using reflectance spectroscopy

Predicting leaf traits across functional groups using reflectance spectroscopy

Which optical traits enable an estimation of tree species diversity based on the Spectral Variation Hypothesis?

Predicting leaf traits across functional groups using reflectance spectroscopy

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