Correlating Spectral Properties (complex mineral samples: 350–15,375 nm, water: 337–823 nm) with Geochemistry and Mineralogy with focus on Acid Mine Drainage (AMD)

Acid mine drainage (AMD) environments host complex mineral-water systems that challenge conventional spectral libraries built for pure minerals. We present an open, harmonized dataset that integrates spectroscopy of real AMD materials with co-registered mineralogical and geochemical measurements from the Kirki (Saint Philippos) mine, NE Greece. The dataset comprises: (i) laboratory mineral reflectance spectra spanning 350–15,375 nm (Visible–Near Infrared (VNIR) – Shortwave Infrared (SWIR) – Mid-Wave Infrared (MWIR) – Longwave Infrared (LWIR) for natural, compositionally diverse complex mineral samples; (ii) in situ water VNIR spectra from 337–823 nm acquired across a gradient of acidity and turbidity; (iii) co-located field measurements (e.g. temperature, pH); and (iv) wide range of laboratory analyses for both solid and water datasets to support ground truth validation. To demonstrate utility, we relate spectral features to mineralogy and composition using partial least squares regression (PLSR). For solids, VNIR–SWIR regions best predict Fe2O3, while MWIR–LWIR features improve estimation of SiO2 and total S, highlighting diagnostic bands across iron oxides/sulfates and silicate frameworks. For waters, VNIR spectra capture acidity/turbidity-driven variability and enable example detecting gradients of dissolved trace metals. Together, these results show that realistic mixture spectra coupled to independent ground truth support quantitative inference of key AMD parameters. These free datasets can facilitate algorithm development, cross-sensor validation, and environmental assessment in AMD-impacted settings, and serves as a high-fidelity analog for planetary spectroscopy, particularly for sulfate- and Fe-oxide detection and LWIR mineral retrievals. All data products, metadata, and processing notes are openly available via the referenced repository. 

The associated data paper is available at: https://www.nature.com/articles/s41597-026-07307-y - citeas

The MultiMiner project is funded by the European Union’s Horizon Europe research and innovations actions programme under Grant Agreement No. 101091374.