Jonas Coussement1,2, Michael Henke3, Peter Lootens1, Isabel Roldán-Ruiz1,4, Kathy Steppe2, Tom De Swaef1. 1. Plant Sciences Unit, Institute of Agricultural, Fisheries and Food Research (ILVO), Melle, Belgium. 2. Laboratory of Plant Ecology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium. 3. Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Stadt Seeland, Germany. 4. Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, Zwijnaarde, Belgium.
Abstract
Background and Aims: Currently, functional-structural plant models (FSPMs) mostly resort to static descriptions of leaf spectral characteristics, which disregard the influence of leaf physiological changes over time. In many crop species, including soybean, these time-dependent physiological changes are of particular importance as leaf chlorophyll content changes with leaf age and vegetative nitrogen is remobilized to the developing fruit during pod filling. Methods: PROSPECT, a model developed to estimate leaf biochemical composition from remote sensing data, is well suited to allow a dynamic approximation of leaf spectral characteristics in terms of leaf composition. In this study, measurements of the chlorophyll content index (CCI) were linked to leaf spectral characteristics within the 400-800 nm range by integrating the PROSPECT model into a soybean FSPM alongside a wavelength-specific light model. Key Results: Straightforward links between the CCI and the parameters of the PROSPECT model allowed us to estimate leaf spectral characteristics with high accuracy using only the CCI as an input. After integration with an FSPM, this allowed digital reconstruction of leaf spectral characteristics on the scale of both individual leaves and the whole canopy. As a result, accurate simulations of light conditions within the canopy were obtained. Conclusions: The proposed approach resulted in a very accurate representation of leaf spectral properties, based on fast and simple measurements of the CCI. Integration of accurate leaf spectral characteristics into a soybean FSPM leads to a better, dynamic understanding of the actual perceived light within the canopy in terms of both light quantity and quality.
Background and Aims: Currently, functional-structural plant models (FSPMs) mostly resort to static descriptions of leaf spectral characteristics, which disregard the influence of leaf physiological changes over time. In many crop species, including soybean, these time-dependent physiological changes are of particular importance as leaf chlorophyll content changes with leaf age and vegetative nitrogen is remobilized to the developing fruit during pod filling. Methods: PROSPECT, a model developed to estimate leaf biochemical composition from remote sensing data, is well suited to allow a dynamic approximation of leaf spectral characteristics in terms of leaf composition. In this study, measurements of the chlorophyll content index (CCI) were linked to leaf spectral characteristics within the 400-800 nm range by integrating the PROSPECT model into a soybean FSPM alongside a wavelength-specific light model. Key Results: Straightforward links between the CCI and the parameters of the PROSPECT model allowed us to estimate leaf spectral characteristics with high accuracy using only the CCI as an input. After integration with an FSPM, this allowed digital reconstruction of leaf spectral characteristics on the scale of both individual leaves and the whole canopy. As a result, accurate simulations of light conditions within the canopy were obtained. Conclusions: The proposed approach resulted in a very accurate representation of leaf spectral properties, based on fast and simple measurements of the CCI. Integration of accurate leaf spectral characteristics into a soybean FSPM leads to a better, dynamic understanding of the actual perceived light within the canopy in terms of both light quantity and quality.
Authors: Gerhard Buck-Sorlin; Pieter H B de Visser; Michael Henke; Vaia Sarlikioti; Gerie W A M van der Heijden; Leo F M Marcelis; Jan Vos Journal: Ann Bot Date: 2011-08-19 Impact factor: 4.357
Authors: Jochem B Evers; Jan Vos; Michaël Chelle; Bruno Andrieu; Christian Fournier; Paul C Struik Journal: New Phytol Date: 2007 Impact factor: 10.151
Authors: Jonathan Vermeiren; Selwyn L Y Villers; Lieve Wittemans; Wendy Vanlommel; Jeroen van Roy; Herman Marien; Jonas R Coussement; Kathy Steppe Journal: Ann Bot Date: 2020-09-14 Impact factor: 4.357