Estimating and mapping tailings properties of the largest iron cluster in China for resource potential and reuse: A new perspective from interpretable CNN model and proposed spectral index based on hyperspectral satellite imagery

作者：Haimei Lei, Nisha Bao, Mei Yu, Yue Cao · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2025 · DOI：10.1016/j.jag.2025.104512 · 被引用次数：14 · 研究领域：Geochemistry and Geologic Mapping、Remote-Sensing Image Classification、Mineral Processing and Grinding

• The spatial distributions of TFe and SiO 2 contents of iron tailings dams were mapped. • A novel spectral index considering the absorption mechanism characteristics was proposed. • The DS algorithm reliably transferred lab-calibrated models to the GF-5 hyperspectral image. • The CNN model yielded the best predictions for the contents of TFe and SiO 2 . Iron tailings are crystalline powders predominantly composed of iron (Fe) and silicon dioxide (SiO 2 ). Spatially characterizing the physical and chemical properties of iron tailings is greatly important for optimal utilization and proper disposal of tailings. Visible-near infrared-shortwave infrared (VIS-NIR-SWIR; 350–2500 nm) spectroscopy offers a rapid, non-destructive, and cost-effective method for quantitatively analyzing tailings properties. This study aimed to quantify and map the spatial distribution of total Fe (TFe) and SiO 2 contents of tailings dams at the largest iron cluster in China using laboratory spectra and GF-5 hyperspectral images. A total of 230 samples were collected from the surface of 11 tailings dams and scanned by a VIS–NIR–SWIR reflectance spectrometer in the laboratory. A novel spectral index was developed through a multi-objective programming methodology. This novel index utilizes band ratios to identify the optimal combination of spectral bands that show a strong correlation with concentrations of TFe and SiO 2 . Simultaneously, it minimizes the impact of moisture content and particle size varia...