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Spectral differences between aqueous solutions of NaCl and KCl have received minimal attention in previous research due to strong similarities between the two salts and the lack of motivation to differentiate between them. Correlations between salinity and absorbance have been developed previously with varying degrees of linearity but have not been tested to saturation. This work will demonstrate that correlating spectral measurements and the concentration of NaCl and KCl in water can be extended up to the saturation point of both salts and that solutions of these salts with unknown concentrations can be distinguished. Spectral data for samples of NaCl and KCl in single-salt solutions were collected up to saturation and correlations were developed for differentiating between solutions of the two species. These correlations were able to correctly identify the solution type for all solutions in the test set and estimate their concentrations with an average error of 0.9%.

Two leaf optical property models, PROSPECT-D and ABM-B, were compared to determine their respective parameter sensitivities and to correlate their parameters. ABM-B was used to generate 150 leaf spectra with various input parameters, and the inversion of PROSPECT-D was used to estimate leaf parameters from these spectra. Wavelength-specific sensitivities were described, and correlations were developed between the leaf pigments and structure parameters of the two models. Of particular importance was the correlation of PROSPECT-D's structure parameter (N) which is a generalized parameter integrating several leaf-level and cell-level characteristics. At the leaf-level, N showed correlations with the leaf thickness and the mesophyll percentage, and at the cell-level, N was affected by the cell cap aspect ratios defined in ABM-B. The estimated value of N also varied substantially with changes in the angle of incidence specified in ABM-B. All of these correlations were nonlinear, and it is unclear how these parameters are combined to affect the final value for N. The correlations developed in this article indicate that additional structural parameters (possibly separated into leaf-level and celllevel) should be considered in future model development that aims to maintain inversion potential while providing more information about the leaf.

Visible-near infrared (VIS-NIR) spectral data are widely used for remotely estimating a number of crop health metrics. In general, these indices and models do not explicitly account for leaf surface characteristics, which themselves can be indicators of plant status or environmental responses. To explicitly include leaf surface characteristics, data are required linking optical properties to surface characteristics. We present the design and experimental validation of a goniospectropolarimeter (GoSPo) that combines the capabilities of a spectrometer, goniometer, and polarimeter. GoSPo was designed with the objective of studying the relationships between leaf surface characteristics and the resulting light reflectance, transmission, and polarization as functions of both direction and VIS-NIR spectra. Using six motors, a pneumatic system, two spectrometers, and a combination of lenses, polarizers, and mirrors, GoSPo can examine a leaf from a particular angle, approximate hemispherical transmittance and reflectance (with root-mean-square error values of 0.0189 and 0.0216 for reflectance and transmittance, respectively, compared to a spectrophotometer and integrating sphere), and obtain spectral polarization measurements without disrupting the sample between measurements. The data collected with GoSPo will aid in model development for remote sensing applications. © The Authors.Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Background Leaf surface phenotypes can indicate plant health and relate to a plant’s adaptations to environmental stresses. Identifying these phenotypes using non-invasive techniques can assist in high-throughput phenotyping and can improve decision making in plant breeding. Identification of these surface phenotypes can also assist in stress identification. Incorporating surface phenotypes into leaf optical modelling can lead to improved biochemical parameter retrieval and species identification. Results In this paper, leaf surface phenotypes are characterized for 349 leaf samples based on polarized light reflectance measured at Brewster’s Angle, and microscopic observation. Four main leaf surface phenotypes (glossy wax, glaucous wax, high trichome density, and glabrous) were identified for the leaf samples. The microscopic and visual observations of the phenotypes were used as ground truth for comparison with the spectral classification. In addition to surface classification, the microscope images were used to assess cell size, shape, and cell cap aspect ratios; these surface attributes were not found to correlate significantly with spectral measurements obtained in this study. Using a quadratic discriminant analysis function, a series of 10,000 classifications were run with the data randomly split between training and testing datasets, with 150 and 199 samples, respectively. The average correct classification rate was 72.9% with a worst-case classification of 60.3%. Conclusions Leaf surface phenotypes were successfully correlated with spectral measurements that can be obtained remotely. Remote identification of these surface phenotypes will improve leaf optical modelling and biochemical parameter estimations. Phenotyping of leaf surfaces can inform plant breeding decisions and assist with plant health monitoring.