## 4 Discussion  
The spectral response interval related to the quantitative inversion of feature parameters is different. According to the characteristic band requirements of parameter inversion, choosing the optimal hyperspectral image fusion process will help to improve the accuracy of quantitative analysis.  
At present, quantitative analysis of soil mainly includes the inversion of organic matter, moisture, and heavy metals, among which the organic-matter-sensitive spectral response is distributed between 800 and 1000 nm [21; 22]. The image fusion technique can be selected using HGF. There are many soil heavy metals, including Hg, Cu, Cr, Cd, Zn, Pb, Sn, Ni, As, Se, and other metalloids, and numerous studies have been conducted on metal pollutants such as Cu, Cr, Cd, Zn, Pb, and As [23]. However, the characteristic bands of different heavy metals tend to vary [24]. In general, the sensitive spectra of Cu, Pb, Cr, and Zn are intermittently distributed in the visible-near-infrared range. For this wavelength range, as discussed in the previous section, HGF or GS is suitable. The characteristic band of As is narrower and is distributed in the near-infrared range of 1000-2400 nm; thus, HGF is applicable. The sensitive spectral range of Cd is the narrowest and distributed at 1300-1400 nm and around 1900 nm, where HGF should be applied. The spectral absorption peaks of soil moisture are concentrated around 1400, 1900, and 2200 nm [25], and thus soil  
Figure 6: Preprocessed images (**a**,**b**) and fused images obtained through (**c**) GS, (**d**) PCA, (**e**) HA, and (**f**) HGF.  
Figure 7: Spectral information of features obtained using different fusion methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Wavelength Range**} & \multicolumn{4}{c}{**390–730 nm**} & \multicolumn{4}{c}{**730–1400 nm**} & \multicolumn{4}{c}{**1400–2260 nm**} \\ \hline \multicolumn{2}{c}{**Method**} & **PCA** & **GS** & **HA** & **HGF** & **PCA** & **GS** & **HA** & **HGF** & **PCA** & **GS** & **HA** & **HGF** \\ \hline \multirow{3}{*}{Evaluation metrics} & CC & \(-0.53\) & **0.79** & 0.71 & 0.71 & 0.10 & 0.74 & 0.76 & **0.78** & \(-0.57\) & 0.79 & 0.78 & **0.79** \\ \cline{2-11}  & STD & 0.89 & 2.40 & **2.81** & 2.73 & 0.56 & 1.89 & 1.85 & **1.89** & 1.42 & **3.79** & 3.51 & 3.45 \\ \cline{1-1} \cline{2-11}  & PSNR & 14.40 & **20.76** & 13.64 & 18.87 & 11.04 & 18.06 & 13.61 & **18.17** & 12.32 & 18.81 & 13.42 & **18.89** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fusion accuracies.  
moisture inversion can be performed using HGF. Quantitative studies on vegetation and water bodies mainly involve the inversion of chlorophyll concentration, and the sensitive spectral features are relatively stable and distributed in the range of 350-800 nm [26; 27], where GS or HGF should be applied.  
This method starts from the spectral dimension, deeply analyzes the characteristics of the harmonic components (harmonic residual term \(A_{0}/2\), amplitude \(C_{h}\), and phase \(\varphi_{h}\)) of the spectral curve, and completes the image fusion while keeping the spectral waveform unchanged, so as to better improve the fidelity of the spectral information and spectral waveform of the fused image. At the same time of image fusion, the guided filtering algorithm is introduced to denoise the spectral curve. After quantitative evaluation, the PSNR index of the fused image at 730-2260 nm was improved. The 2Y1-02D and Sentinel-2B satellite images were used for experimental verification, and the fusion effect evaluation results were consistent with those of the GF-1 and GF-5 images. When the research area is large, the method requires high computer configuration, and the algorithm needs to be further improved to improve efficiency.