The concentrations of arsenic (As) and sulphate (SO4) are important indicators in the assessment of surface water quality, particularly in regions with active mining operations. This study investigates the application of remote sensing techniques for the detection of As and SO4 in water bodies using Earth Observation data. We present a case study conducted on the Chalkidiki peninsula in Greece, encompassing three active surface mines—Olympias, Stratoni, and Skouries—operated by Hellas Gold, a subsidiary of Eldorado Gold Corporation. For this investigation, we employed a small and lightweight VNIR hyperspectral sensor (STS Spectrometer by Ocean Optics, with a spectral range of 337 – 823 nm) mounted on a DJI Phantom 3 drone to capture spectral data at 15 selected locations covering three streams related to the each of the studied mines. The hyperspectral data collection was complemented by in situ surface water sampling, followed by comprehensive laboratory analyses performed at Hellas Gold’s facilities. Due to the common limitations in direct identification of As and SO4 using imaging spectroscopy methods, the focus of our study was on indirect detection of the optically active water substances. For such purpose we utilized in situ measured parameters which showed strong correlation with the presence of these substances and could also be identified spectrally at the same time. Specifically, we targeted Total Suspended Solids (TSS) which strongly correlates with the stream flow. TSS exhibited statistically significant correlation with As concentration (in situ sampling data: R2 = 0.434) and at selected sites with SO4 concentrations (long term water quality measuring: R2 = 0.471 or higher). All these correlations were validated using the Hellas Gold’s regular water quality monitoring data, publicly accessible for the Chalkidiki peninsula (https://environmental.hellas-gold.com/). To estimate TSS (direct estimation) and As, SO4 (indirect estimation) using the acquired hyperspectral UAV spectral data we employed spectral indices (NDVI, NDWI1 and WRI1) adjusted for our spectral data characteristics (e.g., wavelengths of local spectral max/min). These unique spectral features were found between 580-590 nm (green-band), 670-680 nm (red-band) and 708-715 nm (NIR-band). The preliminary results indicate that the proposed spectral ratios, which are sensitive to spectral changes in water caused by TSS, could serve as proxies for estimating concentrations of As and SO4 in surface waters. This study demonstrates the potential of integrating drone-based hyperspectral imaging with in situ measurements for water-quality monitoring in mining-impacted regions, contributing to more effective environmental management practices. Acknowledgments: The presented analysis was conducted under the support of the EC grant MultiMiner. The MultiMiner project is funded by the European Union’s Horizon Europe research and innovation actions programme under Grant Agreement No. 10109137474.
Kýhos et al. (Wed,) studied this question.