**3. Results**

#### *3.1. Full-Range (VNIR, SWIR and LWIR) Absorption Wavelength Mapping and Further Classification*

The intention was to detect the two most pronounced absorptions within the VIS/NIR, SWIR (both HyMap dataset) and LWIR (AHS dataset) regions. To validate if the wavelengths were detected correctly, the absorption wavelength positions were manually derived from 50 different image pixels of the HyMap and AHS data representing different material/surfaces, and they were compared with the absorption wavelength positions of corresponding pixels detected by QaunTools (Figure 6). Only a small deviation in the NIR range (around 0.900 μm) was detected due to the smoothing employed for the VIS/NIR spectral region prior to wavelength mapping. The wavelength positions of all the other absorption features in the SWIR and LWIR regions were detected without any shifts or errors.

**Figure 6.** The manually derived absorption wavelength positions from 50 different image pixels of the the HyMap and the AHS image data representing different material/surfaces compared to the absorption wavelength positions derived automatically when using QaunTools.

As described in the Methods an MNF transformation was employed to the absorption feature mapping results, first when using those derived from the HyMap data only (Scenario 1) and secondly to those derived from both the HyMap and the AHS data (Scenario 2). For each scenario, the ROIs were constructed using RGB colour compositions of the first three MNF bands and these ROIs were consequently used to employ a supervised parallelepiped classification. As a result, eight classes, which were the same for both classifications (Scenarios 1 and 2), were mapped (Figures 7B and 8B). In the case of Scenario 2, where absorption feature parameters derived from both datasets (HyMap and AHS) were used for the consequent mineral mapping, it was possible to map the addition of two classes (Figures 7C and 8C: \*Class 2 and \*Class 9). Figures 9 and 10 show the average class spectrum derived from the HyMap data and the AHS data, respectively. The spectral property of these classes is further discussed and linked with the mineralogy in the following text.

**Figure 7.** Lítov, mineral mapping: (**A**) orthophoto showing the Lítov dump; (**B**) classification using Scenario 1 (absorption feature parameters derived only from the HyMap data were used for the consequent mineral mapping), (**C**) classification using Scenario 2 (absorption feature parameters derived from both datasets, HyMap and AHS, were used for the consequent mineral mapping), (**D**) enlargement of the area of interest. The mapped classes correspond to the mineral classes in Table 1, \*Class 2 and \*Class 9: two additional classes mapped when using Scenario 2.

**Figure 8.** Medard Lake, mineral mapping: (**A**) orthophoto showing the Lítov dump, (**B**) classification using Scenario 1 (absorption feature parameters derived only from the HyMap data were used for the consequent mineral mapping), (**C**) classification using Scenario 2 (absorption feature parameters derived from both datasets, HyMap and AHS, were used for the consequent mineral mapping), (**D**) enlargement of the area of interest. The mapped classes correspond to the mineral classes in the Table 1, \*Class 2 and \*Class 9: two additional classes mapped when using Scenario 2.

**Figure 9.** *Cont.*

**Figure 9.** The average class spectrum derived from the HyMap data: (**A**) the VIS/NIR spectral range (0.450–1.200 μm); (**B**) the SWIR range (2.100–2.400 μm).

**Figure 10.** (**A**) Emissivity of some silicates is displayed using the Arizona University Spectral library [68], the original spectra are displayed together with the equivalent spectra resampled to the spectral resolution of the AHS data. (**B**) The average class spectrum derived from the AHS data. The mapped classes correspond to the mineral classes in Table 1, \*Class 2 and \*Class 9: two additional classes mapped when using Scenario 2.

#### *3.2. Linking the Spectral and Mineral Properties*

In the VIS/NIR region, the average class spectra (Figure 9A) reflect the variations in absorption features characterising diverse iron oxy-hydroxides (secondary minerals with Fe3+) and the organic component (organic C and lignite). Secondary minerals with Fe3+ (hydroxysulfates and oxyhydroxides) exhibit absorption features around 0.500 μm and before 1.00 μm [29,69]. The absorption wavelengths and intensities of the absorption features in this region depend on the nature of the crystal field around the Fe atom and on the nature of the bonds around it, because the nature of the magnetic coupling between the Fe3+ ions (as influenced by the crystal field) facilitates the transition of electrons between energy states [70]. In addition, shifts in the wavelength positions can also reflect material mixing, when the Fe3+ secondary minerals coexist together or with different minerals and an organic component [6]. When interpreting the average class spectra (Figure 9A), it can be seen that Classes 1–3, 5 and 7 exhibit the typical absorption features of secondary Fe3+-bearing minerals. The wavelength position of the first absorption shifts from 0.480 to 0.540 μm, while the second one varies between 0.900 and 1.080 nm. The shift of the second absorption indicates that Classes 1–3 represent the lithology where jarosite is present at higher amounts or coexist together with other Fe3+-bearing minerals, as jarosite exhibits maximum absorption closer to 0.900 μm (shorter wavelengths). On the other hand, the oxyhydroxides (e.g., goethite and hematite) have the second absorption centred around 1.000 μm (longer wavelengths), therefore this mineral is present at high amounts in Classes 4–7 and 10. The absorptions characteristic of organic C and lignite are at 0.550–0.580 μm and 0.720–0.770 μm, respectively [39], and these two are the most visible in the spectra of Classes 4, 8, 9 and 10.

The overtones and combinations of the fundamental OH and H–O–H vibrations can be mainly observed in the SWIR (Figure 9B). In general, the OH combination bands occur due to the two Al cations in the octahedral sites near 2.210 μm; the spectra of kaolinite shows a distinct absorption doublet at 2.170 and 2.210 μm, whereas muscovite has the main absorption at the shorter wavelength (2.200 μm). The absorption around 2.300 μm characterises carbonates [29]; however, no carbonates were identified at primary, secondary or accessory abundances by the XRD analyses conducted for numerous samples collected in Sokolov. Therefore, in this case, additional smaller absorptions at the longer wavelength (around 2.300 and 2.350 μm) are characteristic of the AlFe–OH and Fe2–OH combination bands in phyllosilicates [71]. Kaolinite dominates the spectra of Classes 1–2 and 5–6, whereas Classes 3–4 and 7–10 represent mixtures between kaolinite and muscovite as they have less pronounced absorption at 2.170 μm and flat absorption with the same absorption intensities for the wavelength 2.190 and 2.210 μm.

In the LWIR region absorption features, resulting from fundamental molecular vibration modes, show additional information about mineral constituents, such as Si-bearing minerals (mainly quartz and clay minerals). Quartz, due to the molecular vibrations of the Si–O stretching (reststrahlen bands) displays a broad emissivity doublet in the TIR between 8 and 10 μm [72]. In addition, it is possible to differentiate among diverse clay minerals (e.g., kaolinite, illite, and montmorillonite) using the LWIR range [34]. In Figure 10A the emissivity of some silicates is displayed using the Arizona University Spectral library [68], the original emissivity is displayed together with the equivalent emissivity resampled to the spectral resolution of the AHS data. It shows that the AHS data allows quartz, kaolinite or quartz and kaolinite mixtures to be detected. In the case of the AHS data (Figure 10B), quartz affects the emissivity of the bands centred at 9.25 μm (low emissivity) and 9.75 μm (high emissivity) as well as the slope of the emissivity between these two bands. The distinct absorption features detectable by the AHS bands placed at 9.75 μm and 11.2 μm characterise kaolinite. Muscovite exhibits a wide absorption between 9 and 10 μm, and in general lowers the emissivity between 10–12 μm. It can be concluded that Classes 1 and 9 represent lithologies with a dominant quartz content, Classes 1, 2, 5, 6 and 8 then represent a lithology that has both kaolinite and quartz coexisting together. On the other hand, Classes 4, 7 and 10 predominantly have kaolinite.

The classifications (Figures 7 and 8) were compared to the field documentation and XRD analysis of the samples that fall spatially within each class was carried out. Table 1 shows the mineralogy (XRD

analysis) that characterises six out of 10 defined classes (Classes 1–3, 6, 7 and 10)l for Classes 4, 5, 8 and 9 there were no samples (no XRD analysis) that would fall within their spatial extent. When comparing the mineralogy determined by the XRD with the class spectral properties discussed above, there is good agreement. Kaolinite and quartz represent the dominant minerals for Classes 1–3, whereas jarosite is present together with hematite at secondary abundances, as well as muscovite. However, Class 2 has quartz dominating and was described as a quartz-rich crust developed on the tuffs; on the other hand, Class 3 represents less weathered tuffs exposed by erosion. Class 6 represents the fresh clays of the Cypris formation, where kaolinite is the dominant mineral, followed by quartz and muscovite. Class 7 represents the material of backfill overburden, which was described as quartz-rich hard pack material with a clay matrix also containing lignite fragments. Class 10 represents weathered tuffs in which quartz, muscovite and kaolinite are the dominant, minerals whereas lignite and hematite are present at secondary abundances. Although there were no XRD analyses available for Classes 4, 5, 8 and 9, a mineral description (Table 1) was added; however, it should be emphasised that it is based on the interpretation of the class spectral property (Figures 9 and 10) and in this case it was not possible to differentiate between primary and secondary/accessory abundances of different mineral phases.

**Table 1.** Map classes from Figures 7–10 compared to the XRD analysis and field documentation of the samples that fall spatially within each class. For Classes 4, 5, 8 and 9 there were no XRD analyses available; however, the mineral description based on the interpretation of the spectral property was added.

