## 4 Discussion  
The conditions under which this study was conducted were challenging. As the hyperspectral image was taken approximately long after an oil spill, it was difficult to clearly observe the contamination in the hyperspectral image, both visually and spectrally. Figures 7 and 8 here, and Figure 7 in [7] demonstrate this challenge. Yet, the EROS image clearly depicts the \"historic\" outline of the spill. However, this outline of the spill from December 2014 cannot be fully quantitatively validated today, as there were some remediation efforts to minimize the spill which included pumping-out the oil. Moreover, the time gap of almost two and a half years between the spill and the Fenix hyperspectral image caused some of the oiled areas to be covered by a thin layer of sediments and dust. This can cause uncertainty if one would try to quantitatively assess the method results using recent data. Additionally, the nature of the spilled oil is another compelling reason for using a qualitative evaluation of the classification map as was done here. In our study site, the oil did not appear sporadically, for example in separated areas, rather flowed in continuous narrow channels. This means that in practice, the important thing is to detect the flow pattern rather than the number of oiled pixels in that pattern. Consider two scenarios where one is similar to ours and the other consists of oil that was dumped in several spatially unconnected locations. In the latter scene, failing to detect pixels in one of the dumping locations translates directly to contaminated areas that are not detected by remote sensing. Thus, detecting 50 locations out of 100, for example, means that the accuracy is 50%, and we missed 50 contamination sites. However, in the former scenario (i.e., ours) detecting 50 pixels that are spatially connected out of 100 pixels have the same 50% accuracy. However, here, the quantitative metric is less important because we did not miss the contaminated area, it is only \"narrower\", containing fewer pixels, but it does delineate where the contamination is found. The ANOVA results indicated the best bands that hold diagnostic spectral information to distinguish between oil and clean pixels. The bands for the hyperspectral sensors (excluding CASI) and WV3, and to some extent Self and ASTER, overlap with the PHC-SF as well as the CaCO\({}_{3}\) feature. This implies that the classification was based on the PHC effects on the spectrum of the underlying soil. However, these effects did not express themselves as one would expect (i.e., as the \"classic\" PHC features-see Figure 3 in [7]), but rather in smaller unobservable effects over the spectrum. The two sensors with the limited spectral range (CASI and VENUS) had very low F statistic values, which indicate that they probably do not hold any relevant information for the classification. The low accuracy metrics for these VNIR sensors, as well as the poor classification maps, proved this to be true. The other multispectral sensors showed moderate F statistic values, in comparison to the hyperspectral ones, which can be interpreted as not having sufficient spectral data to achieve good results. This was mainly shown in Figure 7. When considering the relatively coarse GSD of these sensors (LS8, SN2, and ASTER), we can deduce that they are not suitable for the classification needs of this study site. The accuracy metrics from cross-validation and validation procedures as well as the classification maps (Figure 7) that resulted from spectral resampling clearly express the superiority of the hyperspectral sensors over multispectral ones in detecting PHC contamination in our study. This might sound obvious, but the results of WV3 suggest that a multispectral sensor, with adequately located spectral bands, can also achieve good results. Despite minor differences in the cross-validation and validation accuracies between the hyperspectral sensors, we can infer that, in practice, there will be no compelling variation in the outcome of other hyperspectral sensors.