## 4 Discussion
This means that, in our case, such an ample amount of spectral data (Fenix-420 bands) is not mandatory for successful classification, and similar results can be obtained with half (AVIRIS; 224 bands) or even a third (HyMap; 126 bands) of the amount of these spectral bands. These results do not mean that we can start and apply the models on these sensors, as it is mainly simulated data that correspond to 1 m GSD. The importance of these results is that our method can potentially be inclusive to spectral configurations other than the Fenix. This can be applied in practice, for example, when planning a flight mission or even a future sensor that will consist of spectral configuration, like one of the sensors that demonstrated good results here. Perhaps even a drone mission that can carry multispectral sensor with bands like WV3 can achieve good results in similar scenarios. However, this should be validated in future research. The augmentation of our sensors database with two sensors that do not contain information in the PHC-SF proved that sensors with spectral information merely in the VNIR region are unsuitable for this kind of task. However, the results of Self, which has only bands in the SWIR, in comparison with WV3, imply that for multispectral sensors, the VNIR range might hold some relevant information to discriminate between clean and oil pixels. When examining the classification maps produced by the models (especially the ones resulting from the hyperspectral sensors), one might suspect that the classification was based on the mineralogy of the streams and not on the oil spill. Naturally, the oil flowed in the narrow streams of the Reserve and not otherwise. However, as the results indicate, the classification was based on the effect of PHC on the spectrum and the Reserve has many other streams with similar mineralogy composite where the oil did not flow. These streams can be found in the vicinity of the streams that the oil did flow in as well as streams far from it (mainly in the eastern part of the scene). These streams can be seen in Figure 7, and in Figures 8, 11 and 12 in [7]. Notwithstanding, the classification maps focused on the streams where the oil flowed and not in different ones. The spectral simulation for the hyperspectral spaceborne (i.e., EnMap and Hyperion) resulted in high accuracy metrics and a good agreement of the classification maps to the true-color and EROS images, as well as to the other airborne hyperspectral classification maps. Still, when examining their performance considering their native spatial resolution (i.e., 30 m), the classification map does not capture the entire contamination pattern. EnMap and Hyperion have similar spectral and spatial characteristics to future spaceborne hyperspectral instruments; therefore, we can deduce that because of their GSD (30 m), they will not be useful in scenarios like ours. Reference [18] found that 30 m GSD will be sufficient in identifying potentially hazardous acid mine drainage features that cover large areas of at least 75 m in width, which is different from the narrow stream channels in our study site. Overall, when considering all of the parameters (e.g., spectral, spatially, and practicality) required for the task presented in this study, it seems that WV3 might have the characteristics that balance the best between these parameters. It is a multispectral sensor operating in space, with adequately located SWIR bands for PHC-SF detection that collect enough spectral information. This is compatible with the study done by [6], which found the SWIR bands of WV3 to be adequate for PHC detection when the PHC-SF are clearly observable. Additionally, GSD of 7.5 m seems to still capture the outline of the spill, even if it is segmented in some places.