Knowledge-Driven Mapping of Alteration Minerals in Arid Bedrock, East Junggar (NW China): Combining Physical Detectors, Geological Specificity Constraints and Field-Trained Statistics

Mapping alteration minerals from spaceborne hyperspectral imagery in drylands is hindered by mixed pixels, spectrally similar mafic minerals, and false positives from vegetation, soil and iron staining. We present a knowledge-driven workflow combining physical spectral evidence, geological specificity constraints and field-trained random forest statistics. A bedrock and signal-quality mask restricts retrieval to exposed rock; continuum-removed absorption depth, adaptive coherence estimation and endmember orthogonalization provide sub-pixel evidence, while an epidote slope gate and a VNIR copper-carbonate > iron-oxide rule reduce the iron-stain–copper-carbonate overlap from 52.6% to 9.8%. A one-vs-rest random forest trained on 248 field points rescales the evidence, and a union budget confines fifteen channels to ~9% of the bedrock-exposed area. Because the forest is fitted on all field points, the map-level recall of 49.5% is a consistency measure; a leave-one-spatial-band-out cross-validation gives 58.9% (95% CI 48.9–68.3%), against 47.4% for the physical evidence alone, and among the 95 altered points the mineral-specific evidence ranks the recorded mineral above the others with AUC 0.71–0.89. The field labels are macroscopic hand-lens observations rather than laboratory-confirmed mineralogy, and the forest scores are not calibrated probabilities (Brier 0.39, ECE 0.45). In East Junggar the phyllic–argillic assemblage follows the Halasu intermediate–felsic bodies and serpentine–talc along the Kalatongke mafic–ultramafic trend, which separates the two mineral systems; the product is intended for regional screening.