The Xiangshan Uranium Deposit located in Jiangxi Province represents the largest volcanic-hosted uranium deposit in Asia and serves as a critical uranium production base in China. Clay alteration serves as a key prospecting indicator for this deposit, and its quantitative characterization through high-resolution spectroscopy is crucial for elucidating the relationship between hydrothermal activity and uranium mineralization. Addressing the current shortcoming in hyperspectral alteration mapping—which primarily focuses on qualitative mineral identification while lacking systematic quantitative characterization of diagnostic absorption trough parameters—this study utilized ZY1-02E (Chinese Resource Satellite-1-02E) satellite hyperspectral imagery as the data source. Following preprocessing, key parameters of the diagnostic absorption trough at 2205 nm for clay-altered minerals—including depth, area, and symmetry—were quantitatively extracted to construct a spectral index reflecting the intensity of clay alteration. Subsequently, the spatial distribution characteristics of these parameters were systematically analyzed. It was found that, with the line connecting Yankeng, Youjiashan, Xiangshan, and Yunji serving as a boundary, clay alteration generally exhibits a distribution trend of being stronger in the northwest and weaker in the southeast, with morphological features characterized by a combination of strip-like, ring-shaped, and nodular patterns. Based on these findings, nine prospective exploration areas were delineated, including five Class-I and four Class-II prospective exploration areas. High-value anomalies in the absorption trough parameters show good spatial correlation with known uranium deposits and fault structures, effectively indicating the centers of hydrothermal alteration and fluid migration pathways. This study advances the remote sensing identification of clay alteration from qualitative mapping to the quantitative analysis stage, providing a scientific basis for further exploration in the Xiangshan mineralized area. The established method is highly applicable to similar volcanic-type uranium mineralized areas.

Hyperspectral remote sensing enables precise identification of alteration mineral through spectral–image integration and high-resolution capabilities. However, vegetation interference significantly hinders the extraction of alteration information in vegetated areas, thereby posing challenges to the reliable identification of alteration minerals. This study employs GF-5B satellite AHSI imagery acquired in the Huzhou region of Zhejiang Province, China, to address this challenge via a novel Zonal Adaptive Vegetation Suppression Technique (ZAVST). By constructing segmented statistical models that links reflectance characteristics across multiple spectral bands to NDVI values, ZAVST demonstrates an enhanced capability to mitigate vegetation obscuration effects on subsurface lithological features while substantially improving the identification of subtle spectral signatures characteristic of mineralization. Results reveal distinct spatial patterns: Fe-bearing alteration minerals (hematite, pyrite) align along NE-trending faults and volcanic basin margins; Al-OH alterations (montmorillonite, kaolinite) cluster near intrusive contacts; Mg-OH alterations (chlorite, epidote) occur at interfaces between carbonate sequences and concealed intrusions. Composite alteration anomalies exhibiting stacked mineral signatures (up to four distinct types) were identified across the region, demonstrating a strong spatial correlation with known mineralization centers. By integrating alteration zonation, structural lineaments, stratigraphy, geochemical anomalies, and orebody records, this study delineated four priority targets: Lijiaxiang Town, eastern Meixi Town, Miaoxi Town, and the central Moganshan Volcanic Basin.