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The retrieval of plant functional traits from canopy spectra through RTM-inversions and statistical models are both critically affected by plant phenology

Schiefer, Felix ORCID iD icon 1; Schmidtlein, Sebastian ORCID iD icon 1; Kattenborn, Teja 1
1 Institut für Geographie und Geoökologie (IFGG), Karlsruher Institut für Technologie (KIT)


Plant functional traits play a key role in the assessment of ecosystem processes and properties. Optical remote sensing is ascribed a high potential in capturing those traits and their spatiotemporal patterns. In vegetation remote sensing, reflectance-based retrieval methods are either statistical (relying on empirical observations) or physically-based (based on inversions of a radiative transfer model, RTM). Both trait retrieval approaches remain poorly investigated regarding phenology. However, within the phenology of a plant, its leaf constituents, canopy structure, and the presence of phenology-related organs (i.e., flowers or inflorescence) vary considerably – and so does its reflectance. We, therefore, addressed the question of how plant phenology affects the predictive performance of both statistical and RTM-based methods and how this effect differs between traits. For a complete growing season, we weekly measured traits of 45 herbaceous plant species together with hyperspectral canopy reflectance (ASD FieldSpec III). Plants were grown in an experimental setup. The investigated traits comprised Leaf Area Index (LAI) and the leaf traits chlorophyll, anthocyanins, carotenoids, equivalent water thickness, and leaf mass per area. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000125805
Veröffentlicht am 22.03.2021
DOI: 10.1016/j.ecolind.2020.107062
Zitationen: 13
Web of Science
Zitationen: 9
Zitationen: 15
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Geographie und Geoökologie (IFGG)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 02.2021
Sprache Englisch
Identifikator ISSN: 1470-160X, 1872-7034
KITopen-ID: 1000125805
Erschienen in Ecological indicators
Verlag Elsevier B.V.
Band 121
Seiten Art.-Nr.: 107062
Schlagwörter Vegetation remote sensing, Leaf traits, PROSAIL, Partial least squares regression, Flowering, Radiative transfer model
Nachgewiesen in Web of Science
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