## 4 Conclusions  
This study, based on ZY1E/F AHSI data, compares the sensitivity of fire severity estimation using spectral bands and indices from the post-fire image and the pre- and post-fire image combination, employing exhaustive and fixed-step threshold search methods. The results indicate the following: (1) The post-fire image generally exhibited better spectral separation and classification capability compared to the pre-fire and post-fire difference image. The classification accuracy based on spectral differences for the full spectral range in the pre- and post-fire difference image was low, with the highest WF1 of 37.89% at the 731 nm central wavelength. In contrast, the post-fire image had the highest spectral classification accuracy in the near-infrared range (722 nm-1341 nm), with a peak WF1 of 78.99% at the 800 nm central wavelength. (2) dNDI from the pre- and post-fire image combination outperformed VIs derived from the post-fire image and other dVIs constructed from the pre- and post-fire image combination. DI categories in the post-fire image showed good separability and classification accuracy, with the highest WF1 of 79.26% for the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ranking**} & \multicolumn{2}{c}{**dDI Category**} & \multicolumn{2}{c}{**dRI Category**} & \multicolumn{2}{c}{**dNDI Category**} \\ \cline{2-7}  & **Combination/nm** & **WF1/\%** & **Combination/nm** & **WF1/\%** & **Combination/nm** & **WF1/\%** \\ \hline
1 & 1627–1224 & 78.33 & 1543–1241 & 82.75 & 2048–1106 & 83.39 \\
2 & 1627–1241 & 78.02 & 2031–1661 & 82.73 & 2031–1106 & 83.38 \\
3 & 1627–1308 & 77.98 & 1543–1324 & 82.66 & 2199–1073 & 83.33 \\
4 & 1627–1207 & 77.84 & 2048–1610 & 82.63 & 2417–740 & 83.29 \\
5 & 1627–1190 & 77.73 & 2048–1257 & 82.55 & 2031–1056 & 83.22 \\
6 & 1627–1257 & 77.65 & 1745–1241 & 82.54 & 2115–834 & 83.18 \\
7 & 2199–748 & 77.48 & 1745–1324 & 82.54 & 2216–1106 & 83.16 \\
8 & 1644–1190 & 77.40 & 1610–1241 & 82.53 & 2048–1056 & 83.16 \\
9 & 1627–1056 & 77.36 & 1610–1224 & 82.49 & 2401–757 & 83.15 \\
10 & 2199–731 & 77.35 & 2031–1610 & 82.48 & 2216–1089 & 83.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The TOP 10 classification accuracies of different dVIs for the pre- and post-fire image combination. combination of the 757 nm and 611 nm central wavelength. The highest classification accuracy for the pre- and post-fire image combination was achieved by dNDI, with the peak WFI of 83.39% for the combination of the 2048 nm and 1106 nm central wavelength. (3) There is clear spectral and index separability between unburned and burned areas, which allows for effective segmentation. However, the separability between various fire severities in burned areas is poor, particularly between light and moderate-high severities. This lack of distinction is a major limiting factor in fire severity estimation. The complexity of the combustion process in forest and grassland makes it challenging to accurately and comprehensively extract areas with varying fire severities. The key to developing effective fire severity estimation indices lies in fully leveraging the spectral signals associated with various fire severity categories, enhancing the differentiation between severity levels while minimizing the influence of irrelevant information. This study is the first to comprehensively construct all possible indices using three commonly applied index formulation methods based on narrow bands (10 nm or 20 nm) spanning 396 nm to 2495 nm. The optimal spectral bands and indices of ZY-1 hyperspectral image at different time phases have been identified by systematically exploring the relationship between forest fire severity and spectral variations observed via satellite remote sensing. These findings provide valuable guidance for defining spectral settings in future operational fire severity-monitoring satellites and for developing or optimizing fire severity classification indices.