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Variation in reflectance spectroscopy of European beech leaves captures phenology and biological hierarchies despite measurement uncertainties

bioRxiv · 10 Mar 2021 · 10.1101/2021.03.09.434578

Abstract

The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica . We first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over time (2.7 ± 1.7%), or between individuals (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual measured within one week. However, measurements of variation within an individual at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches towards a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance. Highlights We partition biological variation from measurement uncertainty for leaf spectra. Measurement uncertainty represents ca. 3% of spectral variation among beech trees. Biological variation within an individual increases by 80% as leaves mature. Maxima of uncertainty correspond to maxima of biological variation (water content). We recommend procedures to quantify biological variation in spectral measurements.

Plant phenotyping relevance

葉の反射分光測定における不確実性を定量化し、植物形質変異の検出能力と標準化プロトコルを評価する研究であり、フェノタイピング手法が中心です。

abstractWe first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation.
abstractWe suggest best practice approaches towards a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance.

Code and data availability

The paper deposits its analysis scripts and source data in Dryad and its FieldSpec leaf/fabric reflectance spectra in the SPECCHIO spectral database, both publicly accessible with explicit identifiers.

Datasetpublic

umption. The number of 390 replicates is indicated for each dataset. Data processing and statistical analyses were all 391 performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on 392 individual wavelengths; see Results and figure captions for details. Scripts and source data 393 are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec 394 spectroradiometer data are also deposited in SPECCHIO 395 (http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can 396 be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F. 397 Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvat

Open resource ↗Dryad · 10.5061/dryad.gtht76hkx · pdf-raw-page:17 lines:1-49
Datasetpublic

all 391 performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on 392 individual wavelengths; see Results and figure captions for details. Scripts and source data 393 are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec 394 spectroradiometer data are also deposited in SPECCHIO 395 (http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can 396 be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F. 397 Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatica La Massane’ (dataset C), 398 ‘Field spectroscopy F. sylvatica SwissForest’ (dataset D). Visualization and descri

Open resource ↗SPECCHIO · pdf-raw-page:17 lines:1-49

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