TFe and SiO2 Spatial Mapping Enhancement in Iron Tailings: Efficiency of a Calibration Transfer Model

Iron tailings are crystalline powders predominantly composed of iron (Fe) and silicon dioxide (SiO₂). Spatially characterizing of 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. The main objective of this study was to map the spatial distribution of total Fe (TFe) and SiO₂ content in a tailings dam through the use of laboratory spectra and GF-5 hyperspectral imagery based a calibration transfer model approach. A total of 77 samples were collected from the surface of targeting field and scanned by a laboratory VIS–NIR–SWIR reflectance spectrometer. The competitive adaptive re-weighted sampling (CARS) algorithm was applied to select important spectral features. Subsequently, different spectral indices were calculated to enhance the prediction performance of the calibration models. Rulefit and random forest (RF) algorithms were used to calibrate spectral information with associated tailing properties. The results showed that the Rulefit algorithm with selected feature bands and calculated spectral indices yielded the highest estimation accuracy for TFe (R² = 0.86, RMSE = 1.30%, LCCC = 0.87 and bias = -0.45) and SiO₂ (R² = 0.74, RMSE = 2.00%, LCCC = 0.84 and bias = 0.38). The direct standardization (DS) algorithm was applied to correct GF-5 hyperspectral images and enhance the efficiency of calibration model transfer process. Finally, the Rulefit models were transferred to corrected GF-5 hyperspectral images for mapping the spatial distribution of TFe and SiO₂ contents. Our results demonstrated the possibility of successful transfer of laboratory spectral-based model to the GF-5 hyperspectral imagery for mapping spatial distribution of tailing compositions. This finding can be applied for efficiently recovering valuable metals and minimizing environmental risks.