title: Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset.
abstract: Biochemical and structural leaf properties such as chlorophyll content (Chl), nitrogen content (N), leaf water content (LWC), and specific leaf area (SLA) have the benefit to be estimated through nondestructive spectral measurements. Current practices, however, mainly focus on a limited amount of wavelength bands while more information could be extracted from other wavelengths in the full range (400-2500nm) spectrum. In this research, leaf characteristics were estimated from a field-based multi-species dataset, covering a wide range in leaf structures and Chl concentrations. The dataset contains leaves with extremely high Chl concentrations (>100μgcm(-2)), which are seldom estimated. Parameter retrieval was conducted with the machine learning regression algorithm Gaussian Processes (GP), which is able to perform adaptive, nonlinear data fitting for complex datasets. Moreover, insight in relevant bands is provided during the development of a regression model. Consequently, the physical meaning of the model can be explored. Best estimates of SLA, LWC and Chl yielded a best obtained normalized root mean square error of 6.0%, 7.7%, 9.1%, respectively. Several distinct wavebands were chosen across the whole spectrum. A band in the red edge (710nm) appeared to be most important for the estimation of Chl. Interestingly, spectral features related to biochemicals with a structural or carbon storage function (e.g. 1090, 1550, 1670, 1730nm) were found important not only for estimation of SLA, but also for LWC, Chl or N estimation. Similar, Chl estimation was also helped by some wavebands related to water content (950, 1430nm) due to correlation between the parameters. It is shown that leaf parameter retrieval by GP regression is successful, and able to cope with large structural differences between leaves.
authors.affiliation: Department of Bioscience Engineering, Faculty of Sciences, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerpen, Belgium. Electronic address: Shari.VanWittenberghe@ua.ac.be.
authors.forename: Shari
authors.initials: S
authors.lastname: Van Wittenberghe
authors.affiliation: Image Processing Laboratory, University of Valencia, C/ Catedrático José Beltrán 2, E-46980 Paterna, Valencia, Spain.
authors.forename: Jochem
authors.initials: J
authors.lastname: Verrelst
authors.affiliation: Image Processing Laboratory, University of Valencia, C/ Catedrático José Beltrán 2, E-46980 Paterna, Valencia, Spain.
authors.forename: Juan Pablo
authors.initials: JP
authors.lastname: Rivera
authors.affiliation: Image Processing Laboratory, University of Valencia, C/ Catedrático José Beltrán 2, E-46980 Paterna, Valencia, Spain.
authors.forename: Luis
authors.initials: L
authors.lastname: Alonso
authors.affiliation: Image Processing Laboratory, University of Valencia, C/ Catedrático José Beltrán 2, E-46980 Paterna, Valencia, Spain.
authors.forename: José
authors.initials: J
authors.lastname: Moreno
authors.affiliation: Department of Bioscience Engineering, Faculty of Sciences, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerpen, Belgium.
authors.forename: Roeland
authors.initials: R
authors.lastname: Samson
mesh_terms: D000465:Algorithms; D002734:Chlorophyll / Q000737:chemistry; D009584:Nitrogen / Q000737:chemistry; D018515:Plant Leaves / Q000737:chemistry*; D013050:Spectrometry, Fluorescence; D014197:Trees / Q000737:chemistry; D014867:Water / Q000737:chemistry
chemical_list: D014867:Water; D002734:Chlorophyll; D009584:Nitrogen
keywords: Chlorophyll; Hyperspectral; Leaf structure; Leaf water content; Machine learning algorithm; Parameter retrieval; Specific leaf area; Spectral features