**4. Discussion**

Absorption feature parameters—wavelength position and depth—are the most essential information used in spectroscopy; to put it simply, the maximum absorption wavelength position defines what material it is, whereas absorption depth defines its relative abundance. Different absorption feature mapping/matching techniques have been used by the remote sensing community since hyperspectral data were made available. Among these the spectral feature fitting [73] or its improved version—multi-range spectral feature fitting (MRSFF)—are frequently used [74,75]. From the recently developed toolboxes integrating absorption feature matching techniques the Tetracoder [76] or EnGeoMAP 2.0 toolbox [77] should be listed. These approaches are based on the common principle that they compare and statistically assess the fit of the image spectra to the reference spectra. The reference spectra, called endmembers, are scaled to match the image spectra, and can be either laboratory or field

spectral measurements or can be extracted directly from the image (image endmembers). Basically, these classifiers require a routine whereby the endmembers (reference spectra) need to be defined prior to spectral mapping. Moreover, if laboratory or field spectra are used, the successful definition of such endmembers usually requires prior knowledge of the material composition and its spatial distribution within the area of interest. If image endmembers are to be used, and an expert with a background in spectroscopy is required to perform image analysis such as pixel purity analysis (PPI, [78,79]).

In contrast, the approach used here does not require a prior definition of the endmembers or any knowledge of the site conditions; this is a clear advantage. Instead, the absorption wavelength positions and depths of the major absorption features present in different spectral ranges (VNIR/SWIR/LWIR) are extracted automatically and integrated into new raster datasets (multiple absorption feature wavelength and depth matrices). After compressing the main data variability of these new multi-band datasets, using, for instance, an MNF transformation, it is possible to assess the material spatial variability, define training areas (ROIs) and employ a supervised classification. The approach used here is thus unsupervised at the beginning, as no data or knowledge is required prior to absorption feature mapping. Throughout the automatic processing, there is a point where material variability is visualised in a spatial context; afterwards, the training areas (ROIs) for supervised classification are defined. The selection of the spectral ranges under analysis and the definition of the ROIs are the only expert-dependent parts of the analysis. It is thus recommended to divide spectral ranges into VIS/NIR, SWIR and LWIR regions where the diverse mineral groups exhibit distinct absorptions. The definition of the ROIs is rather intuitive as the expert interprets different colour clusters within the image.

Previously, techniques that utilise different types of interpolations for estimating absorption feature wavelength and depth were proposed. Van der Meer [80] proposed a simple linear interpolation technique in order to derive absorption-band position, depth and asymmetry from hyperspectral image. Such parameters were used to interpret the data in terms of the known alteration phases or to estimate heavy metal contents [81]. Rodger et al. [82] proposed a simple quadratic method (SQM) to estimate the wavelengths of absorption features in the shortwave infrared (SWIR) spectral region. The SQM method was tested using spectral data convolved to four different instrument configurations differing in sampling regimes and spectral resolutions. The SQM method was found to estimate feature wavelengths within a reasonable accuracy and to perform well even in noisy environments. Ruitenbeek et al. [83] mapped the wavelength position of the deepest absorption features between 2.100 and 2.400 μm using a second-order polynomial fitting. They concluded that mapping the wavelength position of absorption features between 2.100 and 2.400 μm provided a new method for exploratory analysis of the surface mineralogy and that it would be particularly useful in areas where field validation is sparse and imagery contains shallow spectral absorption features. Moreover, Van der Meer et al. [84] tested two approaches—the 'Wavelength Mapper' [83] and the QUANTools [62] (also used for this study)—and demonstrated for the Rodalquilar epithermal system that deriving absorption feature characteristics, such as the wavelength position and the depth, can be directly linked to mineral type and abundance, and, even more, to subtle changes in mineral chemical composition.

However, as seen from the literature, this is the first demonstration of how the multiple absorption features, and their respective parameters, can be extracted automatically from different spectral ranges (VIS/NIR/SWIR and LWIR), and, furthermore, how, when using this rather simple approach, it is possible to successfully ingrate optical (VNIR and SWIR) and thermal (LWIR) spectral information, gathered in this case by two different sensors. It is also demonstrated how further integration can lead to more complex mineral classification. This is a crucial and highly relevant issue nowadays, when, in addition to the optical sensors, the sensors acquiring the data in the LWIR range are more often available and used (e.g., TASI, AISA OWEL) and even more for the future, when a hyperspectral satellite collecting spectral band through the VIS/NIR/SWIR and LWIR will be operating in orbit (e.g., HySPIRI).

So far the different spectral ranges are more frequently analysed and interpreted separately, without combining the full range of information (VIS/NIR/SWIR/LWIR) into a single integrated

data product. Too few approaches have been proposed allowing for real optical and thermal data integration; still, they require an expert decision to be made prior to spectral mapping (e.g., endmember definition). As previously explained, the approach used here does not require any prior definition of the endmembers; moreover, there is no need for any knowledge or ground data from the site under study.

In the approach used here, the level of noise present in the image datasets can be a limitation. However, in an example of HyMap VIS/NIR data, it was demonstrated that this problem can be minimised by employing spectral smoothing and that it can be tailored specifically to the level of noise present in the data. In addition, there might be other variables that may create false alarms, such as varying topography causing brightness differences across the image produced by shadow and slope variations, varying mineral particle size and soil moisture content. QUANTools process the data in such a way that at the beginning they are normalised by employing the continuum removal method, which helps to remove the effect of scattering. Then the absorption feature wavelengths are mapped and, as long as the noisy/false absorptions are eliminated, the absorption wavelength positions do not tend to change spectral locations as regards shadows or slope variations. The other absorption feature parameter—absorption depth—reflects the material quantity but is also sensitive to the sizes of mineral particles. Therefore, varying grain size is also part of a final classification. It is thus important to decide if the grain size is also a criterion for material mapping or not. If only the chemical composition of the targets is requested, then it is possible to use only absorption feature wavelength matrices in a classification. In this study, 2010 HyMap and 2011 AHS image data were used to classify two sites, Lítov and Medard, which faced no changes regarding the relief and material transport between 2010 and 2011; however, some differences regarding changes in microtopography or moisture content may have been present. Both absorption feature parameters—wavelength and depth—were used for classifications and brought results that were in good agreemen<sup>t</sup> with the XRD analysis, showing that, at a general level, this approach allows diverse minerals to be mapped, including ones that exhibit multiple absorption features though the VIS/NIR/SWIR and LWIR ranges. There are also other useful absorption feature parameters, such as shoulder positions, symmetry or width, that can be used for gathering the information on minor material components, featureless parameters (e.g., heavy metals) or chemical processes/changes. Mapping these additional absorption feature parameters will be a subject for future QUNATools development.

Using this approach, it was possible to integrate the absorption feature information derived from the VIS/NIR/SWIR regions (HyMap data), together with the absorption feature information derived from the LWIR region (AHS data). This integration led to a mineral classification that differentiated between the presence and abundance of diverse Fe3+-bearing minerals and phyllosilicates as well as lignite and quartz contents. In addition to HyMap reflectance, the AHS emissivity data allowed a better discrimination between a quartz-dominating crust and substrates (Classes 2 and 9) from the other classes where quartz did not have such dominating abundances and was present together with other mineral phases at primary or secondary abundance (e.g., Classes 1 and 7).
