## 4 Discussion
This is compatible with [4], who used AVIRIS data in 3.5 and 7.6 m GSD, and had a high accuracy in detecting PHC-SF in marshes following the Deepwater Horizon incident, and with [18], who concluded that a GSD of 2-15 m is required for the identification of acid mine drainage in smaller features such as stream channels and acidic springs/seeps. It is worth mentioning that the currently new generation of orbital hyperspectral sensor with 10 m GSD is planned by both the Italian and the Israeli space agencies--\"SHALOM\"--which is a sensor with 224 bands across 400-2500 nm with a high SNR. It is more likely to assume that SHALOM will serve as an adequate tool to detect PHC-SF from space, and accordingly, may add important applications to the already existing application list issued of SHALOM [19]. It is important to distinguish between the models developed in this paper and their corresponded sensor-specific parameters, and the method (the concept/approach). The method suggests an approach to analyze and classify spectral data, specifically hyperspectral data. This method was developed for cases where the spectral features of a target are not clearly visible in the spectrum or are not as one would expect. This is in contrast to many other studies that showed the expected PHC-SF in their hyperspectral measurements [3; 20; 21; 22; 23; 24]. The method here is not limited to a specific spectral or spatial resolution, it is not limited to crude oil or other contaminant or target, it is an approach we believe can be generalized. This method was developed because the classic/traditional methods (such as spectral angle mapper, continuum removal, derivatives, slopes, indices) completely failed in our case. The models here are sensor-specific, and probably also site and crude oil specific. The coefficients and parameters of each model are a result of applying our method to each sensor. We have developed this method on data from our test site (Evrona Nature Reserve oil spill), but the oil spill can be considered as a case study for our approach. Hence, the models are limited and might be site/contaminant-specific but not the method. Therefore, we believe that the method (and not a specific model) should be the focus of this study and can be inclusive to other scenarios, as it is not sensor-specific or even oil- or soil-specific, and no prior spectral knowledge is required. Even in scenarios where the spectral features of oil are visible, our method can be applied and might even have better results in comparison with the methods that are based on prior spectral knowledge, since our method can find better patterns in the data. On one hand, our method does require some knowledge regarding the location of contaminated and clean targets to calibrate the model, which can be considered a limitation. We used 1800 pixels to train and validate the models and made predictions on over 2 million pixels. On the other hand, if one would have analyzed the Fenix image used in this study by looking for the appearance of the diagnostic PHC-SF, his/her conclusion would be that the entire image is PHC-free, which is obviously not the case. The results should also be considered more broadly than just for the specific sensors used in this study. The sensors represent a variety of operational instruments both from the air and from space. Hence, it should not be interpreted merely as which sensor can be immediately used, rather what are the better spectral and spatial configurations, as well as the limitation for such a challenging study case shown here. The method we applied here should be further investigated in other real scenarios (as opposed to controlled condition), including different types of soil and contaminant, because the type of soil affects the ability to spectrally detect constituents and contaminants, as shown elsewhere [25; 26].