Multi-scale Hyperspectral analysis of mineral distribution in active geothermal field - Námafjall, Iceland

In Iceland, hydrothermal alteration in volcanic rocks results from the interaction of heat, fluids, and surface processes under changing environmental conditions. In particular, the Námafjall geothermal area in northern Iceland hosts active fumaroles, mud pools, and extensive acid–sulphate alteration, resulting in widespread surface mineral distributions. Point-based sampling captures mineralogy at a single location but misses spatial variability, while broader-scale observations do not provide detailed spectral features. To address this, this study evaluates how mineralogical information changes when moving from laboratory measurements to field-based and spaceborne hyperspectral observations, and how these datasets can be linked in an active geothermal environment.

Here, we interpret mineralogy based on integrating laboratory X-ray diffractometer analyses, ASD spectroscopy, laboratory hyperspectral imaging using a SPECIM camera, field-based hyperspectral imaging with HySpex camera, and spaceborne hyperspectral observations from EnMAP. Laboratory analyses identify mineral phases by their diagnostic spectral features, while field-based hyperspectral imaging captures intermediate-scale variability. Spaceborne imagery provides broader-scale mineralogical information but covers only a small area (~30 pixels, each 30 m by 30 m). Each pixel contains mixed surface materials, causing spectral mixing and limiting extraction of distinct minerals at this scale. Hence, to improve mineral identification at field and spaceborne scales, wavelength maps in the SWIR region (2100–2400 nm) were generated to analyse the position of the deepest absorption features across the surface. It helps identify areas where mineralogical information is most likely to be preserved in both field and satellite data.

Based on field observations and the known geology, hydrothermal mineral assemblages at Námafjall are expected to include clays, zeolites, carbonates, sulphates, and native sulphur. But from the preliminary laboratory results of this study, clay minerals and native sulphur were detected in specific samples, while sulphates were not detected. Native sulphur was also observed in field-based hyperspectral data; however, high surface moisture and coarse spatial resolution impacted identification of other mineral classes. To further address uncertainties, spectra will be interpreted after applying linear spectral unmixing and by comparing with spectral libraries. Based on the resulting set of possible minerals at each scale, mineral classification maps will be produced to enable consistent visual comparison of mineral distributions across the three scales.