## 2 Materials and Methods
### Data Processing and Sample Selection  
The selected ZY1E/F AHSI images were preprocessed, including radiometric calibration, atmospheric correction, orthorectification, and geometric registration. The acquired raw data consists of digital number (DN) values, which lack intrinsic physical significance. Therefore, radiometric calibration was first conducted to convert DN values into top-of-atmosphere (TOA) radiance, as defined by Equation (1), using the calibration coefficients provided by the China Centre for Resources Satellite Data and Application. Atmospheric correction was applied to derive surface reflectance, as detailed  
Figure 1: Location of study area. Figure 2: Color stereo of hyperspectral image of burned area by ZY1E AHSI data. in Equations (2) and (3) [43; 44; 45; 46]. The correction was performed using the FLAASH atmospheric correction module in ENVI 5.6.2, based on the MODTRAN radiative transfer model, to account for atmospheric scattering and absorption effects. Considering the influence of atmospheric composition and sensor characteristics, 11 spectral bands severely affected by atmospheric absorption, 4 bands exhibiting anomalous negative values, and 3 bands with spectral overlap were excluded, resulting in a refined dataset comprising 148 spectral bands. Despite these corrections, residual geometric distortions remained due to sensor attitude variations and terrain-induced parallax, potentially affecting the spatial accuracy of fire severity assessments. To mitigate these effects, orthorectification was conducted using 30 m ASTER GDEM V2 data, aligning the imagery with an orthogonal projection and accurately geolocating each pixel. Finally, to facilitate direct comparison with fire severity validation data derived from Sentinel-2 MSI imagery, geometric registration was carried out using a relative registration method, with the pre-fire image as the reference for registering the post-fire image. The overall geometric registration error (RMSE) was less than 1 pixel. The detailed data preprocessing workflow is illustrated in Figure 3.  
\[L=G_{\lambda}\times DN+O_{\lambda} \tag{1}\]  
\[L=(\frac{A\rho}{1-\rho_{e}S})+(\frac{B\rho_{e}}{1-\rho_{e}S})+L_{\alpha} \tag{2}\]  
\[\rho=\frac{(L-L_{\alpha})(1-\rho_{e}S)-B\rho_{e}}{A} \tag{3}\]  
In Equation (1), \(L\) is the atmospheric upper boundary radiance (in units of W\(\cdot\)m\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)um\({}^{-1}\)), and \(G_{\lambda}\) and \(O_{\lambda}\) are the gain coefficient and the offset of different wavelengths (\(\lambda\)), respectively. In Equations (2) and (3), \(\rho\) represents the surface reflectance. All other parameters are automatically calculated by the MODTRAN model and the header file of the data, such as \(\rho_{e}\), is the average surface reflectance of the surrounding pixels. \(S\) is the atmospheric hemispherical reflectance. \(L_{\alpha}\) is the atmospheric path radiance. \(A\) and \(B\) are two coefficients that depend on atmospheric conditions and geometric factors. Figure 3: Schematic diagram of data preprocessing flow. The black solid boxes represent data states, the red dashed boxes indicate operational steps, and the blue dashed boxes denote corresponding processing functions. Pre-fire (11 April 2023, 11:51) and post-fire (21 April 2023, 11:51) Sentinel-2 MSI images were used, with non-vegetated areas masked out. The dNBR for the vegetated areas before and after the fire was calculated. The dNBR values were classified according to previous studies [7; 47], where a dNBR value below 0.299 indicated an unburned area (coded as 1), a value between 0.300 and 0.499 indicated a light severity area (coded as 2), a value between 0.500 and 0.799 indicated a moderate severity area (coded as 3), and a value above 0.800 indicated a high severity area (coded as 4). Other areas were coded as 0. Visual interpretation was performed to ensure the authenticity and validity of the classification, which was then used as the ground truth for fire severity in this study.