Objective In recent years, with the rapid advancement of Earth observation technologies, remote sensing has become an essential tool in geological surveying and mineral exploration. Xizang, as a key region in China’s new round of strategic mineral exploration initiatives, is characterized by low vegetation coverage and favorable surface exposure conditions, which provide advantageous settings for the identification and extraction of alteration anomalies. Therefore, this study focuses on the Mayum area in Xizang to conduct multi−source remote sensing−based alteration extraction and integrated mineral prospectivity prediction.

Methods Multispectral data from Landsat−8 and Sentinel−2, together with GF−5 hyperspectral data, were utilized to extract alteration anomalies using principal component analysis (PCA), band ratio techniques, and mixture tuned matched filtering (MTMF). Meanwhile, 1∶200000−scale geochemical data were processed using isometric log−ratio (ILR) transformation and robust principal component analysis (RPCA), followed by integrated analysis with remote sensing results. Field investigations were conducted for validation.

Results The results indicate that: ① alteration anomalies extracted from Landsat−8 and Sentinel−2 data exhibit strong spatial consistency, with high−intensity anomalies mainly concentrated in the central part of the study area, characterized by extensive iron−staining and hydroxyl alteration zones; ② GF−5 hyperspectral data identified multiple types of alteration information, showing good agreement with multispectral results; ③ remote sensing−derived alteration anomalies demonstrate a strong spatial coupling relationship with geochemical loadings dominated by Hg, Sb, and As, based on which several prospective targets were delineated around known mineralized occurrences; ④ field verification confirmed the presence of magnetitization, malachitization, and epidotization, supporting the reliability of the remote sensing interpretation.

Conclusions The results demonstrate that the proposed multi−source remote sensing approach is effective in extracting alteration information and delineating mineralization−favorable zones, confirming its applicability and reliability for mineral prospectivity prediction in the study area.