Abstract
Plant traits are features that characterise and differentiate between species. From these, certain leaf traits, such as broadleaf or coniferous, have been used to characterise the whole plant, especially when assessing large areas of vegetation. Leaf traits do not only differentiate species but also provide information on plant health. Conventional methods of measuring leaf traits, especially at the molecular level (e.g. water, lignin, and cellulose content), are often expensive and time-consuming. Spectroscopic methods allowing the estimation of leaf traits through empirical models are becoming a tool for accurate estimations of leaf traits. This study identifies the most important bands in the infrared related to biochemical and morphological leaf traits. We generated regression models for eleven leaf traits (including organic and morphological traits), using an optimisation method of partial least squares regression models. This study used spectroscopic data with 6612 bands from the short to the long wave infrared (1.4-16.0μm) from 20 plant species including herbaceous, woody, temperate and tropical plants. Fourteen leaf traits were measured in each fresh leaf, including chemical (e.g., leaf water content, nitrogen, cellulose) and morphological (e.g., leaf area and leaf thickness) traits. Optimized partial least squares regression models were fitted for each leaf trait with a few bands (4-10) as explanatory variables. For these remaining bands, we identified a physiological explanation. From the original pool of leaf traits, cuticle thickness, bundle area and stomata size resulted in R-squared values lower than 70%. These traits were therefore not further considered in the analysis. Eleven leaf traits resulted in optimized models with high R-squared values. The models selected bands from the SWIR where the leaf spectra have features correlated to lignin and cellulose content, which account on average for 28% of the fresh weight of the leaves. The biochemical traits have high R-squares when using the whole spectra (6612 bands) and also in optimized models with five to seven bands. The selected bands match with known molecular bonds of the molecules analysed (water, lignin, cellulose and nitrogen bearing molecules, such as proteins). Morphological leaf traits models have in general good R-squares, especially leaf thickness which has a high correlation with most bands of the SWIR (1.4-2.5 μm) and a specific feature at 7.38μm.
Abstract from the paper’s OpenAlex record; open copy licensed other-oa. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 6
Claim · model-written summary
Models for cuticle thickness, bundle area and stomata size had R-squared below 70%.
Not reportedTaxon
Verbatim
e0“cuticle thickness, bundle area and stomata size resulted in R-squared values lower than 70%”
Claim · model-written summary
Eleven leaf traits had optimized models with high R-squared values.
Not reportedTaxon
Verbatim
e0“Eleven leaf traits resulted in optimized models with high R-squared values”
Claim · model-written summary
Selected SWIR bands correlated with lignin and cellulose content, which averaged 28% of fresh leaf weight.
Not reportedTaxon
Verbatim
e0“selected bands from the SWIR where the leaf spectra have features correlated to lignin and cellulose content, which account on average for 28% of the fresh weight of the leaves”
Claim · model-written summary
Biochemical traits had high model R-squares with full spectra and optimized five to seven bands.
Not reportedTaxon
Verbatim
e0“The biochemical traits have high R-squares when using the whole spectra (6612 bands) and also in optimized models with five to seven bands”
Claim · model-written summary
Selected bands matched known molecular bonds of the analyzed molecules.
- Outcome
- selected bands match with known molecular bondse0
Not reportedTaxon
Verbatim
e0“The selected bands match with known molecular bonds of the molecules analysed (water, lignin, cellulose and nitrogen bearing molecules, such as proteins)”
Claim · model-written summary
Morphological leaf trait models generally had good R-squares, especially thickness, which correlated with most SWIR bands and had a 7.38μm feature.
Not reportedTaxon
Verbatim
e0“Morphological leaf traits models have in general good R-squares, especially leaf thickness which has a high correlation with most bands of the SWIR (1.4-2.5 μm) and a specific feature at 7.38μm”