*3.1. Reflectance Spectra of Healthy and Stressed Leaves*

The average reflectance spectrum of a healthy grapevine vs. stressed grapevine leaves due to heavy metal stress is shown in Figure 2. In the VIS region, the light absorption rate of the stressed grapevine was drastically decreased. This is due to the fact that the spectral characteristics of plants in this region are regularly motivated by pigments [64,65]. Accordingly, this suggests that heavy metal stress reduced pigment contents. Various spectral characteristics between healthy and stressed leaves can also be observed in the RDE, NIR, and MIR regions (Figure 2). As Vogelmann [66], Slaton et al. [23], and Strever [67] stated, plant pigments do not absorb the light in the NIR and MIR regions; therefore, the plant leaf reflectance is significantly increasing in these regions. Additionally, the spectral characteristics of plant leaf in the NIR and MIR regions were changed by structure/morphology and water contents, respectively [54]. According to Figure 2, in the NIR and MIR regions, a lower reflectance was observed in healthy grapevine leaves as opposed to the stressed grapevine. Although other driving variables such as structural parameters and water contents were not measured in this study, it can be concluded that the stress caused by heavy metals had a significant effect on the leaf optical properties.

**Figure 2.** Average reflectance spectrum of healthy grapevine leaves vs. the heavy metal-stressed grapevine leaves (from 350 to 2500 nm).

#### *3.2. Correlation Coe*ffi*cient*

Figure 3 displays the correlation coefficient between grapevine leaves' reflectance (350–2500 nm) and their heavy metal concentrations (Cu, Zn, Pb, Cr, and Cd). The correlation coefficients were noisiest in the range from 350 to 400 nm due to atmospheric effects. Of particular interest is that the highest absolute correlation coefficient took place in the range of 350 to 400 nm in relation to Cr, Pb, and Zn. Cd showed the best correlation with the wavelengths in the VIS region (400–680 nm), while it dropped sharply in the RDE region (680–750 nm) (Figure 3). This suggests that the RDE region is one of the best options for introducing optimal wavelengths to estimate Cd concentrations in grapevine leaves. Also, the other heavy metals caused subtle fluctuations in the RDE region, and their correlation coefficients tended to be positive. This finding indicates the potential of this region to forecast metal contents in the grapevine leaves. Similar correlation coefficients were observed for Cu, Zn, Cr, and Pb in the NIR spectrum region (750–1300 nm), but Cd had a varied correlation curve in this range. In the MIR region (2500–1300 nm), the heavy metal correlation coefficients were closer together (Figure 3). With the exception of Pb, the remaining metals were negatively correlated with most wavelengths of this region.

In comparison to a related study by Zhuang [41], a similar correlation graph between spectral response (400–2500 nm) and heavy metal contents (Cu, Zn, Pb, Cd, As, and Fe) was obtained. A comparison of Figure 3 with the study results of Zhuang [41] shows that the correlation pattern between the heavy metal contents and the spectral response is not alike. Therefore, the structural and biochemical differences between the studied species (grapevine and rice) and the level of spectroscopy (leaf or canopy level) can be considered as the most important drivers justifying these differences.

**Figure 3.** Correlation coefficient between the heavy metal concentration (determined by laboratory analysis) and spectral response of grapevine leaf samples (350 to 2500 nm).

#### *3.3. Optimal Feature Selection*

Determining the optimal wavelengths to monitor the desired plant parameters within the vast hyperspectral bands is one of the most critical operations in spectroscopy [43,46,56]. Commonly, a small number of wavelengths/spectral indices are selected with maximum performance for the study purpose, while missing data should be minimal [46,68]. Thus, we chose the PLS method because of its high adaptability with hyperspectral data to recognize optimal predictive variables (wavelengths and spectral indices) for estimating heavy metals in grapevine leaves [42]. Identifying the fit number of components is one of the most imperative factors in applying the PLS results because the number of components can directly determine the number of model input variables. Accordingly, the cross-validation algorithm was applied to optimize the number of PLS components [43], and then the optimum variable for each of the components was identified. Figure 4 shows the number of optimal components and the wavelength factor loads of the metals studied. This figure shows that the numbers of developed fit components were 4, 6, 5, 6, and 4, for Cu, Zn, Pb, Cr, and Cd, respectively. Therefore, based on the introduced components, the wavelengths and spectral indices which had the highest correlation with the components were identified. They can be subsequently used as optimal spectral wavelengths and indices in the relevant modelling process, especially for estimating metal concentrations in the grape leaves [43].

As shown in Figure 4, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm can be recognized as an optimal rate for estimating Cu content in grapevine leaves. In the same method, wavelengths in the vicinity of 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were the optimal wavelengths for estimating Zn, Pb, Cr, and Cd, respectively. Based on these results, the VIS, RDE, NIR, and MIR regions introduced eight, eight, three, and five wavelengths for estimating the studied heavy metals, respectively. The most delicate regions to estimate the studied heavy metals in the grapevine leaves were RDE and VIS (particularly the blue region). Consistent with this finding, Liu et al. [38] and Zhuang [41] also reported that VIS and RDE delivered the most optimal wavelengths for estimating heavy metal contents. Moreover, according to the results, the RDE was one of the most influential regions in introducing optimal wavelengths for estimating the contents of Zn, Pb, Cr, and Cd. In confirmation with this finding, Gu et al. [36] noted the RDE region as being sensitive to estimate the variances of metal contents (especially Cd). They suggested the wavelength of 782 nm as an optimal wavelength for estimating Cd concentration in *Brassica rapa* leaves.

**Figure 4.** *Cont.*

**Figure 4.** The factor load of wavelengths (350–2500 nm) in the optimal components extracted by the the partial least squares (PLS) method for estimating heavy metal concentrations (from top to bottom) in the grapevine leaves (vertical axis is the factor load).

In the same way, the optimal spectral indices for estimating contents of Cu, Zn, Pb, Cr, and Cd were also determined based on the interpretation of the PLS results. In Table 2, a summary of the PLS results is presented, which is used to determine the optimal indices to estimate the heavy metal concentrations. As an optimal index for the estimation of Pb, Cr, and Cd concentrations, the Structure Intensive Pigment Index (SIPI) (proposed by Penuelas et al. [69]), which represents the ratio of carotenoids to chlorophyll, was the most frequent index among the studied indices. Furthermore, the Disease Water Stress Index (DWSI) and Moisture Stress Index (MSI) indices, which are sensitive to water levels in vegetation (water stress), were identified as optimal indices for estimating Zn–Pb and Cu–Cd, respectively. It is worth remarking that the Normalized Difference Vegetation Index (NDVI) was not chosen as the optimal index to predict the studied metal contents. On the other hand, according to Zhuang [41], the NDVI band ratios were extremely useful in monitoring the contents of metals in the paddy canopy. Therefore, it can be argued that, in addition to the structural and biochemical differences between grapevine and paddy species, the differences in studied spectral indices are another reason for differences in the optimal spectral indices.


**Table 2.** Summary of the PLS results on the number of components and optimal indices for estimating heavy metal contents in grapevine leaves.
