Abstract
Due to the absence of evident absorption features and low concentrations, the copper (Cu) concentration in plant leaves has rarely been estimated from hyperspectral remote sensing data. The capability of remotely-sensed estimation of foliar Cu concentrations largely depends on its close relation to foliar chlorophyll concentration. To enhance the subtle spectral changes related to chlorophyll concentration under Cu stress, this study described a wavelet-based area parameter (SWT (605−720), the sum of reconstructed detail reflectance at fourth decomposition level over 605−720 nm using discrete wavelet transform) from the canopy hyperspectral reflectance (350−2500 nm, N = 71) of Carex (C. cinerascens). The results showed that Cu concentrations had negative and strong correlation with chlorophyll concentrations (r = －0.719, p < 0.001). Based on 1000 random dataset partitioning experiments, the 1000 linear calibration models provided a mean R2Val (determination coefficient of validation) value of 0.706 and an RPD (residual prediction deviation) value of 1.75 for Cu estimation. The bootstrapping and ANOVA test results showed that SWT (605−720) significantly (p < 0.05) outperformed published chlorophyll-related and wavelet-based spectral parameters. It was concluded here that the wavelet-based area parameter (i.e., SWT (605−720)) has potential ability to indirectly estimate Cu concentrations in Carex leaves through the strong correlation between Cu and chlorophyll. The method presented in this pilot study may be used to estimate the concentrations of other heavy metals. However, further research is needed to test its transferability and robustness for estimating Cu concentrations on other plant species in different biological and environmental conditions.
Highlights
A certain amount of heavy metals in plants is essential for building enzymes and proteins, which are beneficial to plant growth and development [1]
With samples and reflectance spectra of plants collected under field conditions, t his study aimed to (i) explore the relationship between Cu and chlorophyll concentration in Carex (C. cinerascens) leaves, (ii) describe a wavelet-based area parameter from canopy reflectance of Carex to enhance the subtle spectral changes related to chlorophyll concentration under Cu stress, and use it to indirectly estimate foliar Cu concentrations, and (iii) compare the performance of the wavelet-based area parameter to that of some classical chlorophyll-related and wavelet-based spectral parameters in Cu estimation
According to the findings reported by Blackburn and Ferwerda [9], first derivative reflectance spectra can be more suitable as input for wavelet transform (WT) analysis than original reflectance spectra, due to the strengthening of feature peaks and valleys by derivative analysis
Summary
A certain amount of heavy metals (e.g., copper) in plants is essential for building enzymes and proteins, which are beneficial to plant growth and development [1]. The concentration of heavy metals in plant leaves is an important indicator of pollution status in surrounding environment [5,6]. Biochemical (e.g., chlorophyll and nitrogen) [8,9,10] properties of plants at the leaf, canopy, and landscape levels. Numerous studies have focused on the retrieval of the spectrally active biochemical properties of plants (e.g., water, chlorophyll, and nitrogen) using physical or statistical models [8,9,10,11,12,13,14,15,16,17,18,19,20]. Much less progress has been made to estimate foliar heavy metal concentrations [21,22,23,24,25], which may be due to their very low concentrations in plant leaves
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