Evidence map›Paper›PMID 34758853›Full record

ArticlePlant methods2021

Leaf reflectance can surrogate foliar economics better than physiological traits across macrophyte species.

Paolo Villa, Rossano Bolpagni, Monica Pinardi, Viktor R Tóth

Abstract read
In one paragraph

Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Paolo VillaInstitute for Electromagnetic Sensing of the Environment, National Research Council of Italy (CNR-IREA), Milan, Italy. villa.p@irea.cnr.it.ORCID http://orcid.org/0000-0002-6952-836X
Rossano BolpagniInstitute for Electromagnetic Sensing of the Environment, National Research Council of Italy (CNR-IREA), Milan, Italy.
Monica PinardiInstitute for Electromagnetic Sensing of the Environment, National Research Council of Italy (CNR-IREA), Milan, Italy.
Viktor R TóthBalaton Limnological Research Institute, Tihany, Hungary. toth.viktor@blki.hu.

Funding

FP7 Space 730066Nemzeti Kutatási Fejlesztési és Innovációs Hivatal 135832
6 · The paper itself

Abstract

backgroundMacrophytes are key players in aquatic ecosystems diversity, but knowledge on variability of their functional traits, among and within species, is still limited. Remote sensing is a high-throughput, feasible option for characterizing plant traits at different scales, provided that reliable spectroscopy models are calibrated with congruous empirical data, but existing applications are biased towards terrestrial plants. We sampled leaves from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400-2500 nm) and leaf traits (dealing with photophysiology, pigments, and structure). We explored optimal spectral band combinations and established non-parametric reflectance-based models for selected traits, eventually showing how airborne hyperspectral data could capture spatial-temporal macrophyte variability.

resultsOur key finding is that structural-leaf dry matter content, leaf mass per area-and biochemical-chlorophyll-a content and chlorophylls to carotenoids ratio-traits can be surrogated by leaf reflectance with normalized error under 17% across macrophyte species. On the other hand, the performance of reflectance-based models for photophysiological traits substantively varies, depending on macrophyte species and target parameters.

conclusionsOur main results show the link between leaf reflectance and leaf economics (structure and biochemistry) for aquatic plants, thus envisioning a crucial role for remote sensing in enhancing the level of detail of macrophyte functional diversity analysis to intra-site and intra-species scales. At the same time, we highlighted some difficulties in establishing a general link between reflectance and photosynthetic performance under high environmental heterogeneity, potentially opening further investigation directions.

Indexed as

Aquatic plantsFunctional traitsIntraspecific variabilityLeaf economics spectrum (LES)Remote sensingSpectroscopy

Identifiers

PMID34758853
PMCPMC8582205

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.