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

Leaf mass per area (LMA), leaf dry matter content (LDMC) and leaf water content/ equivalent water thickness (EWT) are commonly used functional plant traits in ecology. Whereas spectroscopy has recently proven to be a powerful tool to collect such functional trait information across large scales, it...

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Autores principales:	Kenny Helsen, Leonardo Bassi, Hannes Feilhauer, Teja Kattenborn, Hajime Matsushima, Elisa Van Cleemput, Ben Somers, Olivier Honnay
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Intraspecific trait variation Leaf dry matter content Leaf mass per area Leaf water content Equivalent water thickness Partial least squares regression (PLSR) Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/6c2476b9508446d4b299f8108e535e68
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spelling	oai:doaj.org-article:6c2476b9508446d4b299f8108e535e682021-12-01T04:59:21ZEvaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance1470-160X10.1016/j.ecolind.2021.108111https://doaj.org/article/6c2476b9508446d4b299f8108e535e682021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21007767https://doaj.org/toc/1470-160XLeaf mass per area (LMA), leaf dry matter content (LDMC) and leaf water content/ equivalent water thickness (EWT) are commonly used functional plant traits in ecology. Whereas spectroscopy has recently proven to be a powerful tool to collect such functional trait information across large scales, it remains unclear whether these reflectance-based trait predictions are accurate enough to reliably model trait variation at the intraspecific level (i.e. across individuals of one species). We explored the potential of hyperspectral leaf reflectance-based methods to predict LMA, LDMC and EWT at the intraspecific level for two herbs (Hieracium umbellatum and Jacobaea vulgaris) and two shrubs (Rosa rugosa and Rubus caesius), based on 2400 leaf samples. More specifically we tested i) inversion of the PROSPECT-D radiative transfer model, ii) a generic PLSR approach using the multibiome LMA PLSR model and iii) a data-specific PLSR approach at the species level. For the latter approach we furthermore assessed both model transferability across species and the trade-off between sample size and model accuracy. Although the PROSPECT-D model inversion and the multibiome LMA PLSR model were relatively accurate for intraspecific LMA predictions of shrubs (R2 > 71 and 76%, respectively, however NRMSE = 33–47%), their performance was lower for herbs (R2 < 61%, NRMSE = 28–50%). PROSPECT-D was furthermore slightly less successful in retrieving EWT at the intraspecific level (R2 < 70%, NRMSE = 16–43%), and unsuccessful in retrieving LDMC through combining LMA and EWT inversion results (R2 < 10%, NRMSE = 9–192%). The highest correlation accuracy was obtained for all three traits with the species-specific PLSR models (R2 > 70%, NRMSE < 10%). If high predictive accuracy is needed, we thus suggest the use of species-specific PLSR models. The training of species-specific PLSR models comes at the cost of a needed sample size of 100–160 leaves however, depending on the trait. Although transferability of species-specific PLSR models seems limited overall, our results suggest potentially high transferability across herbaceous species.Kenny HelsenLeonardo BassiHannes FeilhauerTeja KattenbornHajime MatsushimaElisa Van CleemputBen SomersOlivier HonnayElsevierarticleIntraspecific trait variationLeaf dry matter contentLeaf mass per areaLeaf water contentEquivalent water thicknessPartial least squares regression (PLSR)EcologyQH540-549.5ENEcological Indicators, Vol 130, Iss , Pp 108111- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Intraspecific trait variation Leaf dry matter content Leaf mass per area Leaf water content Equivalent water thickness Partial least squares regression (PLSR) Ecology QH540-549.5
spellingShingle	Intraspecific trait variation Leaf dry matter content Leaf mass per area Leaf water content Equivalent water thickness Partial least squares regression (PLSR) Ecology QH540-549.5 Kenny Helsen Leonardo Bassi Hannes Feilhauer Teja Kattenborn Hajime Matsushima Elisa Van Cleemput Ben Somers Olivier Honnay Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance
description	Leaf mass per area (LMA), leaf dry matter content (LDMC) and leaf water content/ equivalent water thickness (EWT) are commonly used functional plant traits in ecology. Whereas spectroscopy has recently proven to be a powerful tool to collect such functional trait information across large scales, it remains unclear whether these reflectance-based trait predictions are accurate enough to reliably model trait variation at the intraspecific level (i.e. across individuals of one species). We explored the potential of hyperspectral leaf reflectance-based methods to predict LMA, LDMC and EWT at the intraspecific level for two herbs (Hieracium umbellatum and Jacobaea vulgaris) and two shrubs (Rosa rugosa and Rubus caesius), based on 2400 leaf samples. More specifically we tested i) inversion of the PROSPECT-D radiative transfer model, ii) a generic PLSR approach using the multibiome LMA PLSR model and iii) a data-specific PLSR approach at the species level. For the latter approach we furthermore assessed both model transferability across species and the trade-off between sample size and model accuracy. Although the PROSPECT-D model inversion and the multibiome LMA PLSR model were relatively accurate for intraspecific LMA predictions of shrubs (R2 > 71 and 76%, respectively, however NRMSE = 33–47%), their performance was lower for herbs (R2 < 61%, NRMSE = 28–50%). PROSPECT-D was furthermore slightly less successful in retrieving EWT at the intraspecific level (R2 < 70%, NRMSE = 16–43%), and unsuccessful in retrieving LDMC through combining LMA and EWT inversion results (R2 < 10%, NRMSE = 9–192%). The highest correlation accuracy was obtained for all three traits with the species-specific PLSR models (R2 > 70%, NRMSE < 10%). If high predictive accuracy is needed, we thus suggest the use of species-specific PLSR models. The training of species-specific PLSR models comes at the cost of a needed sample size of 100–160 leaves however, depending on the trait. Although transferability of species-specific PLSR models seems limited overall, our results suggest potentially high transferability across herbaceous species.
format	article
author	Kenny Helsen Leonardo Bassi Hannes Feilhauer Teja Kattenborn Hajime Matsushima Elisa Van Cleemput Ben Somers Olivier Honnay
author_facet	Kenny Helsen Leonardo Bassi Hannes Feilhauer Teja Kattenborn Hajime Matsushima Elisa Van Cleemput Ben Somers Olivier Honnay
author_sort	Kenny Helsen
title	Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance
title_short	Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance
title_full	Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance
title_fullStr	Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance
title_full_unstemmed	Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance
title_sort	evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/6c2476b9508446d4b299f8108e535e68
work_keys_str_mv	AT kennyhelsen evaluatingdifferentmethodsforretrievingintraspecificleaftraitvariationfromhyperspectralleafreflectance AT leonardobassi evaluatingdifferentmethodsforretrievingintraspecificleaftraitvariationfromhyperspectralleafreflectance AT hannesfeilhauer evaluatingdifferentmethodsforretrievingintraspecificleaftraitvariationfromhyperspectralleafreflectance AT tejakattenborn evaluatingdifferentmethodsforretrievingintraspecificleaftraitvariationfromhyperspectralleafreflectance AT hajimematsushima evaluatingdifferentmethodsforretrievingintraspecificleaftraitvariationfromhyperspectralleafreflectance AT elisavancleemput evaluatingdifferentmethodsforretrievingintraspecificleaftraitvariationfromhyperspectralleafreflectance AT bensomers evaluatingdifferentmethodsforretrievingintraspecificleaftraitvariationfromhyperspectralleafreflectance AT olivierhonnay evaluatingdifferentmethodsforretrievingintraspecificleaftraitvariationfromhyperspectralleafreflectance
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Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance

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