Hyperspectral satellite missions such as EnMAP, PRISMA and Tanager have made imaging spectroscopy widely accessible, yet their heterogeneous formats, high dimensionality and demanding preprocessing requirements still hinder efficient scientific use. To address these challenges, we developed i.hyper, a multimodule addon for GRASS designed to provide a harmonized, reproducible and scalable workflow for hyperspectral data. The methodology integrates a unified 3D raster data model with per-band metadata, ensuring consistent handling of wavelength, FWHM, validity and radiometric units across sensors. A modular preprocessing engine implements established imaging spectroscopy and machine-learning techniques, including Savitzky–Golay smoothing, baseline correction, continuum removal and several linear and nonlinear dimensionality-reduction methods such as PCA and Nyström approximation—executed spectrally while preserving spatial alignment. The resulting toolset comprises the modules i.hyper.import, i.hyper.preproc, i.hyper.composite, i.hyper.explore and i.hyper.export, which together implement a complete workflow from sensor-specific import through preprocessing, visualization and export. The modules resolve practical gaps such as mixed multi-file product structures and metadata conventions, scalable nonlinear reduction and the need for integrated spectral exploration. The addon is available as an official extension in the GRASS repository, enabling direct integration of imaging spectroscopy into established geospatial workflows. This work demonstrates that GRASS can serve as an efficient environment for hyperspectral processing, reducing the data-engineering overhead that typically precedes scientific modelling, and providing a foundation for further preprocessing steps performed by the powerful, native functionalities that GRASS offers.
Mangafić et al. (Wed,) studied this question.