Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence datasetGaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset
Faculty of Sciences. Bioscience Engineering
Environmental Ecology & Microbiology (ENdEMIC)
Journal of photochemistry and photobiology: B: biology. - Lausanne
134(2014), p. 37-48
University of Antwerp
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 (400-2500 nm) 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 (>100 mu g cm(-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 (710 nm) 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, 1730 nm) 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, 1430 nm) 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. (C) 2014 Elsevier B.V. All rights reserved.