Our study addresses the need for universal monitoring solutions given the diverse environmental impacts of surface mining operations. We present a solution combining remote sensing and machine learning techniques, utilizing a dataset of over 2000 satellite images annotated with ten distinct labels indicating mining area components. We tested various approaches to develop comprehensive yet universal machine learning models for mining area segmentation. This involved considering different types of mines, raw materials, and geographical locations. We evaluated multiple satellite data set combinations to determine optimal outcomes. The results suggest that radar and multispectral data fusion did not significantly improve the models’ performance, and the addition of further channels led to the degradation of the metrics. Despite variations in mine type or extracted material, the models’ effectiveness remained within an Intersection over Union value range of 0.65–0.75. Further, in this research, we conducted a detailed visual analysis of the models’ outcomes to identify areas requiring additional attention, contributing to the discourse on effective mining area monitoring and management methodologies. The visual examination of models’ outputs provides insights for future model enhancement and highlights unique segmentation challenges within mining areas.

Spectral similarity measures such as the Spectral Angle Mapper (SAM) treat all spectral bands equally, an assumption difficult to justify for modern imaging spectroscopy data where measurement uncertainty varies strongly with wavelength. We introduce wSAM, an uncertainty-weighted extension of SAM that incorporates per-band observational uncertainty as inverse-variance weights. On 47 EMIT–RadCalNet observation pairs across five sites, wSAM (w = 1/σ²) reduces mean spectral angle by 49% relative to SAM and outperforms both hard band masking and alternative weighting schemes, while a cross-site discriminability analysis confirms that weighting sharpens rather than erases spectral differences between surface types.