## 5 Conclusions  
In this study, the Jianghuai hilly region is set as an example to predict the sand, silt, and clay content of soil texture in the surface layer of arable land using GF5 AHSI hyperspectral satellite images and laboratory spectra with machine learning models. By contrasting PCA-PLSR and PCA-CNN, the performance of hyperspectral data for soil texture content prediction was assessed. The following are the key conclusions:  
In the Jianghuai hilly area, soil texture is mainly composed of clay and silt. Sand, clay, and silt have similar spectral characteristics in the VNIR-SWIR reflection region with strong water vapor absorption near 1400 nm, 1900 nm, and 2200 nm. The results found that the prediction accuracy of clay content in soil texture was higher than that of sand and silt by using GF5 AHSI images and ASD spectra in hilly areas.  
Compared with PCA-PLSR, PCA-CNN has a 32.5%, 24.4%, and 39.0% higher prediction index R\({}^{2}\) for sand, silt, and clay particles in the surface soil texture of cultivated land in hilly areas, while RMSE has decreased by 27.3%, 19.4%, and 57.0%, respectively. PCA-CNN has a strong nonlinear approximation ability. When predicting the content of sand, silt, and clay particles, it can effectively extract the characteristics of the input data and avoid spectral auto-correlation between different components of soil texture through multiple convolution kernels in the convolution layer perform convolution operations on the input spectral principal component data, which can better solve the problem of spectral unmixing among multiple soil texture forms.  
According to the results of the remote sensing inversion, the sand grains of surface soil texture in the study area are mainly distributed in the west, the silt grains are distributed in the east, and some clay grains are scattered in the south and north, which are consistent with the soil mechanical composition of the sampling points. This indicates that there is a higher potential for quantitative monitoring of soil texture features using hyperspectral satellite remote sensing.  
In summary, the PCA-CNN model can provide a fast and efficient monitoring method for predicting the surface soil texture content of cultivated land in the Jianghuai hilly area, greatly reducing the cost and time of soil texture detection, and providing an intelligent method for a thorough and dynamic understanding of regional soil quality. In this study, the GF5 satellite is sensitive to environmental factors such as terrain, vegetation and atmosphere in the retrieval process, and PCA-CNN still has limitations such as data dependency, lack of physical mechanism, and interpretability of spectral unmixing algorithm in spectral unmixing. Therefore, the practicality of the model still needs be further verified in different regions and soil properties [49].  
Conceptualization and writing original draft preparation: B.P. and S.C.; methodology, supervision, project administration, funding acquisition: B.P. and F.X.; field test: M.Z. and H.Y.; data analysis: J.D., H.Y. and H.C.; writing--review and editing supported by B.P. and S.D. All authors have read and agreed to the published version of the manuscript.  
The author gratefully acknowledges the financial support from Anhui Natural Science Research Foundation (2208085 US14, 1708085 MD90), Anhui University Collaborative Innovation Fund (GXXT-2019-047), Natural Science Foundation of colleges, and universities in Anhui Province (KJ2020)D07, KJ2020)D09.  
Not applicable.  
The authors declare no conflict of interest in the publication of this paper.