This paper describes the design, implementation, and usage of a Python package called Hyperspectral Python (HypPy). Proprietary software for processing hyperspectral images is expensive, and tools developed using these packages cannot be freely distributed. The idea of HypPy is to be able to process hyperspectral images using free and open-source software. HypPy was developed using Python and relies on the array-processing capabilities of packages like NumPy and SciPy. HypPy was designed with practical imaging spectrometry in mind and has implemented a number of novel ideas. To name a few of these ideas, HypPy has BandMath and SpectralMath tools for processing images and spectra using Python statements, can process spectral libraries as if they were images, and can address bands by wavelength rather than band number. We expect HypPy to be beneficial for research, education, and projects using hyperspectral data because it is flexible and versatile.

Hydrothermal processes modify the chemical and mineralogical composition of rock. We studied and quantified the effects of hydrothermal processes on the composition of volcanic rocks by a novel application of the Shannon entropy, which is a measure of uncertainty and commonly applied in information theory. We show here that the Shannon entropies calculated on major elemental chemical composition data and short-wave infrared (SWIR) reflectance spectra of hydrothermally altered rocks are lower than unaltered rocks with a comparable primary composition. The lowering of the Shannon entropy indicates chemical and spectral sorting during hydrothermal alteration of rocks. The hydrothermal processes described in this study present a natural mechanism for transforming energy from heat to increased order in rock. The increased order is manifest as the increased sorting of chemical elements and SWIR absorption features of the rock, and can be measured and quantified by the Shannon entropy. The results are useful for the study of hydrothermal mineral deposits, early life environments and the effects of hydrothermal processes on rocks.

Strategies to interpret short-wave infrared hyperspectral images of rocks involve the application of analysis and classification steps that guide the extraction of geological and mineralogical information with the aim of creating mineral maps. Pre-existing strategies often rely on the use of statistical measures between reference and image spectra that are scene dependent. Therefore, classification thresholds based on statistical measures to create mineral maps are also scene dependent. This is problematic because thresholds must be adjusted between images to produce mineral maps of the same accuracy. We developed an innovative, knowledge-based strategy to perform mineralogical analyses and create classifications that overcome this problem by using physics-based wavelength positions of absorption features that are invariant between scenes as the main sources of mineral information. The strategy to interpret short-wave infrared hyperspectral images of rocks is implemented using the open source Hyperspectral Python package (HypPy) and demonstrated on a series of hyperspectral images of hydrothermally altered rock samples. The results show how expert knowledge can be embedded into a standardized processing chain to develop reproducible mineral maps without relying on statistical matching criteria.

Within the framework of ESA's Earth Observation Program, the Medium Resolution Imaging Spectrometer (MERIS) is developed as one of the payload components of the ENVISAT‐1. MERIS is a fully programmable imaging spectrometer, however a standard 15 channel band set will be transmitted for each 300 m. pixel (over land) covering the visible and near‐infrared wavelength range. Since MERIS is a multidisciplinary sensor providing data that can be input into ecosystem models at various scales, we studied MERIS performance relative to the scale of observation using simulated data sets degraded to various resolutions in the range of 12m. to 300m. Algorithms to simulate MERIS data using airborne imaging spectrometer data sets are presented, including a case study from GERIS 63 channel data over a agricultural site in central Spain (the Almaden test site). Through a pixel purity analysis, end members are derived from the MERIS‐type data and subsequently used as input to a spectral unmixing analysis yielding fraction of end member (abundance) images. The original data as well as the abundance images are spatially analyzed using variogram surfaces and mapping accuracy is modeled at various spatial scales. We observe differences between the sampling resolutions (i.e., pixel size) found to be optimal for the different ground cover types. The optimal scale for observing different components of spectral mixtures varies depending on the type of mixture, however, the best possible resolutions in all cases of mixtures studied is below the envisaged 300 m. field of view for the MERIS sensor. The analysis of semivariogram surfaces demonstrates that the spatial distribution of the variance of the mixtures is invariant with scale, thus the observed mapping discrepancies are not related to the data processing but to the observations themselves.

Strategies to interpret short-wave infrared hyperspectral images of rocks involve the application of analysis and classification steps that guide the extraction of geological and mineralogical information with the ultimate aim of creating mineral maps. Previous interpretation strategies often rely on the use of statistical measures between reference and image spectra, which are scene dependent. Therefore, classification thresholds based on statistical measures to create mineral maps are also scene dependent, which is problematic because thresholds have to be adjusted between images to produce mineral maps of the same accuracy. We developed an innovative, knowledge-based interpretation strategy to perform mineralogical analyses and create classifications that overcome this limitation by using physics-based wavelength positions of absorption features, which are invariant between scenes, as the main sources of mineral information. The interpretation strategy is implemented using the open source Hyperspectral Python package (HypPy) and demonstrated on a series of laboratory-acquired hyperspectral images of hydrothermally altered rock samples. The results show how expert knowledge can be embedded into a standardized processing chain to develop reproducible mineral maps without relying on statistical matching criteria.