## 4 Methods
### The HySpex Data Processing  
The HySpex data processing mainly includes two parts: mineralogy mapping and chlorite spectral absorption features extraction (Figure 6b).  
High-grade iron ore is mainly composed of magnetite and pyrite whose spectral absorption features are concentrated in VNIR. Band math is a common remote sensing method to distinguish the opaque metal minerals [34]. High-grade iron ore is mainly composed of magnetite and pyrite. According to the spectra of pyrite and magnetite in VNIR image (Figure 10a), the model is expressed as \(K_{1}\) (Band 10-Band 50). The \(K_{1}\) is the slope of the spectral reflectance from Band 10 to Band 50. Obviously, \(K_{1}\) of pyrite is greater than 0, whereas less than 0 for magnetite. BIFs ore is mainly composed of magnetite and quartz, and the slope of the spectral reflectance from Band 20 to Band 70 are quite different (Figure 10b). The model distinguishing magnetite and quartz is then expressed as \(K_{2}\) (Band 20-Band 70) \(<-0.5\) for quartz and \(-0.5<K_{2}<0\) (Band 20-Band 70) for magnetite.  
The main diagnostic spectral absorption features of altered minerals such as chlorite, muscovite, and biotite are located in SWIR. \"MNF-PPI-n Dimensional,\" a common hyperspectral remote sensing processing method [35; 36], was used to extract the endmember spectra from SWIR images. According to  
Figure 10: The visible and near-infrared spectral range (VNIR) spectra of magnetite, pyrite, and quartz: (**a**) spectra of pyrite and magnetite in Q-2 and (**b**) spectra of quartz and magnetite in Q-12.  
Figure 9: The results of Savitzky–Golay filtering: (**a**) smooth result of 5 points and 2 order and (**b**) smooth result of 6 points and 2 order. A larger window will minimize the subtle spectral features (blue box).  
the previous research on Qidashan Iron Deposit, the spectra of minerals that may exist in the mining area were selected from USGS spectral library as the customer-defined spectral library. The Spectral Angle Mapping (SAM), which has proven to be an effective classification method for hyperspectral images [10; 11; 28], was adopted to determine the spectral similarity between the endmember spectra and the customer-defined spectra by calculating the angle between the spectra so that the endmember spectra can be identified [37].The operator then checked the results of the SAM based on geological and spectral knowledge to ensure that the endmember spectra were not misidentified. The maximum angle of 0.15 rad was used as the threshold for the SAM mineralogy mapping in this paper. By combining knowledge of mineral spectroscopy, pixels with spectral angle higher than threshold were determined by manual inspection until no unclassified pixels remained.  
The result map of mineral classification was converted to vector, then the chlorite was extracted and other minerals were masked to obtain the pure chlorite thematic map. The continuum-removal was used to extract the absorption wavelength position and depth of chlorite. The continuum is a \"shell\" wrapped around the spectrum (Figure 11). The continuum-removal can remove the \"background spectrum\" to highlight the subtle absorption features, which is beneficial to the extraction and comparison of the spectral features of different minerals.