## 6 Discussion
### Methodology  
The correct identification of the most representative endmember spectra as well as their number is undoubtedly the most critical step for a successful SU. Endmember identification is also a challenging procedure in the application of SU when working in natural heterogeneous environments or when using data of low dimensionality. The endmember spectra that are extracted from the same image are much more associated with the specific image features compared to library spectra. In addition, they capture the inherent effects related to the sensor and image acquisition conditions. This is the reason why in this study we adopted this option in order to extract four of the five endmembers. However, two important practical limitations of the endmember identification process originate from the low spatial resolution (30 m) of the data as well as the limited number of spectral bands, which is generally the case when working with multispectral data. In this study, this problem was evident when attempting to identify \"pure\" signatures of hyrodacitic lavas, basaltic andesites (of scarce and very localized outcrops), and alluvial/beach deposits. Although the selected signatures were based on the geological map, they were most probably not representative of a single material, but were the spectral products of variable proportions of more than one composition (e.g., basaltic andesites and alluvial/beach deposits) or even different degrees of hydrothermal alteration. It was impossible to find totally pure endmembers in a 30 m pixel size across this particular area, which added a degree of uncertainty on the final SU results. This can be seen on the similarities between the hyrodacite  
Figure 7: Comparison of the spectral signatures of each endmember in the spectral bands of each satellite sensor: (**a**) hyodacites, (**b**) basaltic andesites, (**c**) alluvial deposits, (**d**) hydroxyl-bearing alteration, and (**e**) sulfur. Each material’s spectral signatures show significant similarities (especially hyodacites and basaltic andesites) in all three sensors. and basaltic anddesite endmembers extracted from the ASTER and Landsat-8/OLI images (Figure 4a,b). However, this difficulty did not seem to have a significant influence on the SU results. Another general important issue in SU is the discriminability of endmembers, which depends on the spectral characteristics of the dataset, such as the number and position of the spectral bands in the electromagnetic spectrum. In the present case, there was the example of the spectral signatures of the two lava endmembers in the ASTER image, which showed a similar pattern but they were well distinguished by Sentinel-2 MSI due to Sentinel's larger number of spectral bands in the VNIR region of the spectrum (Figure 4a,c). The same applies to the case of the spectral signatures of the alluvial deposits, lavas, and the hydrothermal alteration products. An additional common confounding factor of the SU approach is within-class spectral variability especially when working with low-dimensionality data. This is why, in this study, we addressed this issue by selecting the average spectral signature of a \(9\times 9\) ROIs for the three primary lithology endmembers. Although this approach alleviates statistical fluctuations (up to some degree) in the specification of an endmember, there is still a degree of uncertainty in the unmixing results. An additional difficulty we faced in this study was the quantitative evaluation of the results. To the best of our knowledge, the only reference information about the study area was the sketch map provided in [68]. Thus, in order to perform quantitative evaluation of the results, we proceeded to the digitization, georeferencing, co-registration and creation of a classification map of it. Although the resulting map was not an ideal one (due to the errors inserted during the previous processes such as the lack of reference points), it was the only way to implement quantitative evaluations.