**1. Introduction**

Modern remote sensing has become a novel tool, not only for detecting and quantifying geological materials [1], but also for monitoring dynamic processes and induced changes in their physical/chemical properties [2–6]. Multispectral and superspectral imagery have been effectively used for mapping geology/minerals [7–11] as well as for monitoring mining impacts [12–19]. However, with a low number of rather broad spectral bands, these systems provide only discrete spectral information (e.g., the state-of-the-art Sentinel-2 sensor has 13 spectral bands [20]. On the other hand, data with very high spectral resolution (hundreds of narrow bands)—known in the remote sensing community as hyperspectral (HS) or imaging spectroscopy (IS) data—are nowadays capable of providing a continuous spectrum throughout the whole spectral range (0.4–13 μm). These systems are mainly available for aerial data acquisition; however, new hyperspectral satellite systems will be lunched in the near future [21–25].

The IS data can cover different spectral ranges from visible (VIS, 0.4–0.7 μm) through the near infrared (NIR, 0.7–1.0 μm) and shortwave infrared (SWIR, 1.0–2.5 μm) to even longer wavelengths of the thermal region (longwave infrared: LWIR, 8–13 μm). Within the VIS/NIR/SWIR/LWIR regions

specific or combined absorptions (called absorption features from now on) can be found, caused by the electronic transition of Fe-bearing minerals (VIS/NIR region) and by the molecular vibration of specific chemical groups (e.g., OH<sup>−</sup>, CO3, Si–O) (SWIR and LWIR spectral regions). Considering the main mineralogical groups, the VIS/NIR parts of the electromagnetic (EMS) spectrum allow for mapping surfaces with a high concentration of Fe3+-bearing minerals (e.g., hematite, goethite and jarosite) [6,26–28] and SWIR is useful in detecting carbonates, clay minerals and salts [29–31]. On the other hand, the VNIR and SWIR portions of the EMS are not optimal for detecting the main constituents of igneous rocks, quartz and feldspars due to their lack of absorption features in the optical part of the EMS. These minerals can be mapped using the thermal LWIR region [32–36].

Clearly, optical and thermal IS data, when used together, allow different varieties of minerals to be mapped and thus allow lithology mapping in a more complex way. The synergy effect of merging both ranges (optical and thermal) was demonstrated in examples of soil proximal sensing [37–39]. Considering mineral mapping, as stated by McDowell and Kruse [40], the majority of the previous work exploiting spectral IS data has focused on data from a single wavelength range, typically the VNIR, SWIR or LWIR. Few studies have taken advantage of data from the full VIS/NIR, SWIR, and LWIR spectral range, whereas the different spectral ranges were analysed and interpreted separately [41–43] and the full-range information was not actually combined into a single integrated data product. This limits the complexity of the final interpretation as spectral and spatial associations or patterns may be too complex to be seen by the naked eye and thus may remain hidden.

Recently, Kruse [41] proposed integrating the individual mapping results derived from AVIRIS (VIS/NIR/SWIR) and HyTES data (LWIR) and combining them using geologically directed logical operators. In the following study by McDowell and Kruse [40], spectral information from the individual VIS, NIR, SWIR and LWIR ranges was first analysed independently and then the resulting compositional information, in the form of image endmembers and apparent abundances, was integrated using ISODATA cluster analysis. They demonstrated that the integrated map provided additional compositional information that was not evident in the VIS, NIR, SWIR, or LWIR data alone, and concluded that their analysis allowed for more complete and accurate compositional mapping.

This study tested whether the multiple absorption features, which are directly linked to the mineral composition and are present though the VIS/NIR/SWIR and LWIR ranges, can be:


To map multiple absorption feature parameters automatically, a toolbox was used that was developed using Interactive Data Language (IDL). The biggest advantage of such an approach is that it allows the issue of prior definition of the endmembers to be overcome; this is a requested routine used for all widely-used spectral mapping techniques (e.g., Spectral angle mapping SAM [44], Spectral feature fitting SFF e.g., [45,46] and Spectral unmixing [47,48]. Two different airborne image datasets were analysed, HyMap (HyVista Corp., Australian airborne imaging spectrometer, VIS/NIR/SWIR image data) and Airborne Hyperspectral Scanner (AHS, LWIR image data), both datasets were acquired over the Sokolov open-cast lignite mines in the Czech Republic. It is further demonstrated that even in this case, when the absorption feature information derived from multispectral LWIR data is integrated with the absorption feature information derived from hyperspectral VIS/NIR/SWIR data, it is an important contribution and improvement in terms of more complex mineral mapping.

#### **2. Materials and Methods**

## *2.1. Test Site*

The study was performed in the Sokolov basin in the western part of the Czech Republic (Figure 1), in a region affected by long-term extensive lignite mining. The basement of the Sokolov Basin is formed

of pre-Variscan and Variscan metamorphic complexes (recorded metamorphism from Devonian to Lower Carboniferous periods) of the Eger, Erzgebirge, Slavkov Forest, Thuring-Vogtland Crystalline Units and granitoids of the Karlovy Vary Pluton. The upper portions of these rocks are frequently weathered to kaolinitic residue. The basal late Eocene Staré Sedlo Formation is formed of well-sorted fluvial sandstones and conglomerates and is overlain by a volcano–sedimentary complex up to 350 m thick, which contains three lignite seams with variable sulphur (S) content. Long-term open cast mining required the removal of up to 180 m of thick overburden (Cypris clays), which was stockpiled and replaced after the lignite was extracted. At the dumps, the material consists mostly of weathered volcanic tuffs and Cypris clays, which can be characterised as well-laminated clays with a dominant kaolinite content; however, different varieties of mineralogical composition are common (e.g., the presence of montmorillonite, illite with admixtures of Ca–Mg–Fe carbonates, sulphates, sulphides, analcite, Mg–micas and bitumen [49]. Due to the presence of S in the coal, both active and abandoned lignite mines are affected by acid mine drainage (AMD) [50,51].

**Figure 1.** Geographic position of the two sites under study: Lítov dump and Medard Lake, Sokolov basin, Czech Republic.

Considering the Sokolov site, under various research projects (HypSo, EO-MINERS, DeMinTIR), numerous studies have been published demonstrating how hyperspectral imaging data can be utilised to quantitatively model the substrate pH [6,52], map mineral composition [41], estimate mine water pollution [53] and assess tree health [54–56]. This study focused on the Lítov dump and the abandoned open pit called Medard (Figure 1), as no human activities were conducted between 2010 and 2011 at these two sites, the years when the two aerial image datasets, HyMap and AHS, were acquired, thus these two sites faced no changes regarding the relief and material transport between 2010 and 2011.
