Evaluation of different types of satellite images for the identification of minerals formed by Acid Mine Drainage using Spectral Angle Mapping (SAM)

Acid mine drainage (AMD) is a severe environmental issue associated with the generation of finely milled rock waste containing high concentrations of sulfide minerals and potentially toxic elements (PTEs) during mining activities. The formation of secondary minerals, such as sulfates and iron oxyhydroxides, results from sulfide oxidation and subsequent acid neutralization by carbonate and silicate minerals, making them key indicators of AMD. Efficient identification of these minerals is crucial for monitoring their impact on soils.
This study compares the capabilities of Landsat-09, ASTER, and Sentinel-2 satellite images in identifying Jarosite, Goethite, Ferrihydrite, Anhydrite, and Gypsum (associated with AMD) using the "Spectral Angle Mapping" (SAM) technique. SAM is a spectral analysis method that classifies materials based on the angle between spectral vectors corresponding to their spectral signatures.
The evaluated satellite images were selected based on their spatial, spectral, and temporal resolution. Their strengths and limitations in detecting the selected secondary minerals were assessed using the SAM technique in ENVI software. The algorithm was trained with spectral signatures ranging from 0.4 to 2.5 micrometers, obtained from the USGS and ASTER spectral libraries. Landsat-09 offers moderate resolution and global coverage; ASTER excels in shortwave infrared capabilities but lacks recent satellite imagery for current analyses; and Sentinel-2 combines high resolution with a broad spectral range and biweekly temporal resolution, with continuous image acquisition to date.
The results show significant differences in each sensor's ability to identify the minerals of interest. Sentinel-2 demonstrated high accuracy due to its spatial resolution and specific spectral bands. Conversely, ASTER was unable to precisely delineate pixels associated with the requested minerals. Lastly, Landsat-09 showed limitations in mineral identification using this technique due to the sensor’s spatial resolution. This study highlights that spatial resolution is the most critical factor in selecting satellite imagery for SAM applications. Thus Sentinel-2, with the highest spatial resolution (10 m) achieved superior results in identifying AMD-related minerals.
This study provides guidance for selecting satellite sensors based on spatial and spectral resolutions in studies aimed at mineral identification using SAM. It contributes to the development of more efficient strategies for environmental management, mineral exploration, and energy resource studies, among other applications.