## 4 Discussion  
Among the two models mentioned above, the PCA-CNN model has better prediction results for the sand, silt, and clay content in soil texture inversion than the PCA-PLSR model as a whole. The PCA-CNN prediction model for sand, silt, and clay content based on the ASD spectral data was 32.5%, 24.4%, and 39.0% higher than the R\({}^{2}\) of PCA-PLSR, and the RMSE decreased by 27.3%, 19.4%, and 57.0%, respectively. And the RPD of all three PCA-CNN models is above 1.5. For the GF5 AHSI data, the R\({}^{2}\) of the PCA-CNN model  
Figure 10: Inversion of soil texture content based on GF5 spectral data PCA-CNN algorithm ((**a**) sand, (**b**) silt, (**c**) clay). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of Data** & & **ASD** & & & **GF5 AHSI** & \\
**Type of Soil** & **Sand** & **Silt** & **Clay** & **Sand** & **Silt** & **Clay** \\ \hline Min (g/kg) & 4.86 & 74.46 & 12.01 & 15.11 & 123.55 & 11.82 \\ Max (g/kg) & 958.57 & 742.94 & 869.88 & 834.79 & 763.34 & 831.32 \\ Mean (g/kg) & 470.79 & 577.02 & 239.28 & 121.42 & 542.49 & 327.83 \\ Range (g/kg) & 953.71 & 668.48 & 857.87 & 819.68 & 639.79 & 819.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics of the model inversion results based on the PCA-CNN algorithm. Figure 9: Inversion of soil texture content based on measured spectral data PCA-CNN algorithm ((**a**) sand, (**b**) silt, (**c**) clay). was 49.6%, 50.3%, and 79.8% higher than that of the PCA-PLSR, the RMSE decreased by 42.0%, 35.4%, and 57.1%, and the RPD increased by 74.2%, 46.8%, and 177.0%, respectively. This is consistent with the results of [PERSON] et al. and [PERSON] et al., who used a machine learning model based on VIS-NIR-SWIR spectroscopy to predict soil texture content and also obtained good prediction results [26; 30]. Compared with the results of [PERSON] et al., they used three algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient progression (XGB) to estimate the content of soil texture and predict that the R\({}^{2}\) of the optimal model reached 0.79, indicating that PCA-CNN has better non-linear unmixing and prediction capabilities than traditional machine learning [15]. Compared with PCA-PLSR, PCA-CNN has significant advantages in spectral unmixing and model prediction. It can automatically extract spatial and spectral features from spectral data through convolution and pooling layers. This allows the model to better capture the complex relationships between spectral data and the content of sand, silt, and clay. Because of its ability to capture local perception, non-linear modelling, and global information capture, CNN can more accurately perform spectral unmixing and simulate the non-linear relationship of complex spectra. At the same time, introducing PCA can effectively reduce feature dimensions, weaken computational complexity, and prevent overfitting that enables PCA-CNN to construct the model more accurately for complex nonlinear relationships, and it is suitable for solving the spectral overlap and diversity problems of sand, silt, and clay [46]. In contrast, PCA-PLSR, as a traditional spectral unmixing method, is usually based on linear assumptions and has poor modeling performance for complex nonlinear relationships, although it has certain applications in spectral unmixing. Significant errors may occur when PLSR is used to deal with nonlinear relationships. In addition, PCA-PLSR has high requirements for data quality, feature selection, and model parameter selection, which may require multiple pre-processing and adjustments. When processing high-dimensional spectral data, the computational complexity is high, resulting in certain limitations of this algorithm [47]. Therefore, in predicting soil texture content, PCA-CNN can better solve the nonlinear fitting problem between various soil texture forms.