## 1 Introduction
Therefore, the first objective of this study was to examine the performance of the model developed in [7] on other hyperspectral airborne systems. Nevertheless, in practice, airborne hyperspectral images might not always be a feasible option. Although they have a high spectral and spatial resolution, generating such images can be expensive, operationally demanding, and limited in aerial coverage, in comparison to spaceborne sensors. Spaceborne sensors are more practical in that sense, as they have the advantage of zero operational costs (for the end-user), and they are not subject to airborne limitations, such as the need to request flight permissions, which can sometimes impede the entire airborne campaign. Additionally, they can be targeted quickly and produce wide-swath images, even over dangerous or restricted zones. Subsequently, the second objective of this study was to test the model on hyperspectral spaceborne sensors. Yet, to date, the amount of space-based hyperspectral sensors remains relatively low. This ought to change in the near future, with the expected launch of the Environmental Mapping and Analysis Program (EnMAP [8]); the Spaceborne Hyperspectral Applicative Land and Ocean Mission (SHALOM [9]); the Hyperspectral Precursor of the Application Mission (PRISMA [10]), which is already in orbit; the Hyperspectral Imager Suite (HISUI [11]); and the Hyperspectral Imaging Mission for the Environment (CHIME [12]), among additional sensors. Notwithstanding, a tradeoff exists between the high spectral resolution of these spaceborne sensors (>100 spectral bands) and the coarse GSD (30 m), in comparison to airborne sensors or even to multi-spectral sensors in-orbit such as Sentinel-2 (10 m; hereafter SN2) and WV3 (7.5 m). A spatial resolution of 30 m could be insufficient for a successful classification, especially in cases of relatively small-scale pollution. Conversely, the operational multispectral sensors in space are more abundant than the hyperspectral ones. They have the same space-oriented advantages, typically combined with a better GSD, and hence, are more commonly used for various practical applications. However, they have a much lower spectral resolution that falls short in capturing the spectral shape of the PHC-SF, except for WV3, which has bands that overlap the PHC-SF at -1200, -1700, and -2300 nm. Because of the nature of the previously mentioned model, which can discover an intricate structure within the data, independently of prior spectral knowledge, the third objective of this study was to explore the potential of the multispectral sensors to achieve good classification results. With the above considerations, we embarked on exploring the potential of multi- and hyperspectral air- and spaceborne sensors to successfully detect PHC under low signal and challenging conditions. The purpose of this study was to examine whether this method is robust enough to work under an assortment of spectral and spatial configurations. To that end, we have conducted several simulations based on the Fenix hyperspectral image where we spectrally resampled the image to eleven different sensors, as well as spatial resample. We commenced on this research with intending to answer several questions, namely: Can the method in [7] be applied to other airborne or spaceborne hyperspectral systems with a lower spectral and spatial resolution? Can it be applied to multispectral sensors, even those with only a few spectral bands, or even none, in the PHC-SF range (1000-2500 nm)? Is the vast spectral information found in hyperspectral systems necessary to successfully map crude oil contamination, or could multispectral sensors possibly achieve similar results to hyperspectral?