ArticleEcology and evolution2014
Predicting leaf traits of herbaceous species from their spectral characteristics.
Article in Ecology and evolution, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Multi-trait spectral modeling for estimating grapevine leaf traits and nutrients.Plant phenomics (Washington, D.C.) · 2025Article
- A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.Frontiers in plant science · 2022Article
- Frond Optical Properties of the FernPlants (Basel, Switzerland) · 2020Article
- Phytoplankton community, structure and succession delineated by partial least square regression in Daya Bay, South China Sea.Ecotoxicology (London, England) · 2020Article
- Leaf N and P stoichiometry in relation to leaf shape and plant size for Quercus acutissima provenances across China.Scientific reports · 2017Article
- Genome-wide association mapping of soybean chlorophyll traits based on canopy spectral reflectance and leaf extracts.BMC plant biology · 2016Article
- Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Trait predictions from leaf spectral properties are mainly applied to tree species, while herbaceous systems received little attention in this topic. Whether similar trait-spectrum relations can be derived for herbaceous plants that differ strongly in growing strategy and environmental constraints is therefore unknown. We used partial least squares regression to relate key traits to leaf spectra (reflectance, transmittance, and absorbance) for 35 herbaceous species, sampled from a wide range of environmental conditions. Specific Leaf Area and nutrient-related traits (N and P content) were poorly predicted from any spectrum, although N prediction improved when expressed on a per area basis (mg/m(2) leaf surface) instead of mass basis (mg/g dry matter). Leaf dry matter content was moderately to good correlated with spectra. We explain our results by the range of environmental constraints encountered by herbaceous species; both N and P limitations as well as a range of light and water availabilities occurred. This weakened the relation between the measured response traits and the leaf constituents that are truly responsible for leaf spectral behavior. Indeed, N predictions improve considering solely upper or under canopy species. Therefore, trait predictions in herbaceous systems should focus on traits relating to dry matter content and the true, underlying drivers of spectral properties.
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