## 1 Introduction  
Anthropogenic crude oil pollution is a global problem, endangering many crucial natural resources, such as drinking water and arable land, as well as causing environmental damage in diverse ecological systems. The detection of crude oil pollution on terrestrial surfaces using hyperspectral remote sensing relies primarily on the chemical structure of the organic compounds found in crude oil, termed petroleum hydrocarbons (PHCs). Because of its chemical structure (C-H bonds), PHCs give rise to spectral manifestations in the shortwave infrared (SWIR; 1000-2500 nm) portion of the electromagnetic spectrum, specifically at around 1200, 1700, and 2300 nm [1]. These PHC spectral features (hereafter termed PHC-SF), can be detected mainly by utilizing hyperspectral sensors, which record the amount of reflected energy in tens or hundreds of continuous narrow spectral bands on a spatial domain, allowing for the identification of the PHC-SF. Several studies have shown the competency of airborne hyperspectral sensors to identify the PHC-SF in the soil [2; 3; 4; 5]. In addition, [PERSON] et al. [6] demonstrated that the Advanced SpaceborneThermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (hereafter WV3), which are multispectral spaceborne sensors, have the potential to resolve the PHC-SF, by convolving the hyperspectral data to their wavelengths. The initial setting for all of these studies was the expected clear appearance of the PHC-SF in the spectra, which guided their analysis. However, this is not always the case, as demonstrated by [7]. Small-scale leakages, low levels of pollution, oil spills that occurred a long time ago, and other factors such as the type of substrate (i.e., type of soil) and even seasonal dust coverage, might fail to clearly observe the PHC-SF from the air or space. Previous mentioned work done by researchers in this did not account for such cases, probably because they did not encounter them, and their methods will most likely fail since they are based on the specific shape of PHC-SF. As the crude oil pollution is rather stable over time, the lack of clear observation of PHC-SF does not mean that the PHC pollution is not present in the field and cannot be detected using remote sensing; rather, in such cases, an alternative approach needs to be taken to overcome this challenge. Such an approach was developed and successfully deployed on a hyperspectral image by [7]. In that study, the hyperspectral image was acquired using the AisaFENIX-1K sensor (Specim, Oulu, Finland, hereafter termed Fenix) approximately two and a half years after an oil spill occurred in an arid area in southern Israel. Because of various factors, the PHC-SF were not visible in the image spectra, even though they were captured by in-situ measurements using a field spectrometer and lab measurements. Consequently, a machine learning approach was developed, based on spectral standardization, band selection, dimension reduction, and model training. All of these techniques were guided by statistical methods, rather than prior knowledge of the spectral shape and location of PHC-SF. The main idea behind this approach was to train a model based on a known in-situ location of a set of pixels labeled ('oil' and 'clean') and to let the machine learn the relationship between each class and its corresponding spectra. The mapping of the PHC contamination was successful. This can be attributed to the high-quality Fenix sensor used to obtain the hyperspectral image. This sensor has a high signal-to-noise ratio (SNR), 420 spectral bands in the 400-2500 nm range, which produced a hyperspectral image with fine ground sampling distance (GSD) of 1 m.  
However, the full potential of the model was not fully explored, as it was not tested on other sensors that naturally have different spectral and spatial configurations. For example, the airborne visible/infrared imaging spectrometer (AVIRIS) and the Hyperspectral Mapper (HyMap) have 224 and 126 spectral bands, respectively, and different SNRs.