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| Study on Quantitative Estimation of Soil Dry Density by Hyperspectral Method After Removing the Influence of Moisture |
| LI Xiao-fang1*, HUO Jian-hong1, JIANG Nan5, WANG Yan-cang2, 3, 4, GU Xiao-he3, HAO Hong-chun1, LI Zi-tong1, HAN Rui-xin1, WANG Jin-gao1, GAI Xiao-kai1, WANG Yao-xin1 |
1. Langfang Normal University, Langfang 065000, China
2. North China Institute of Aerospace Engineering，Langfang 065000, China
3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
4. National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China
5. Hebei Institute of Geological Survey and Mapping (Spatial Information Technology Application Research Center, Hebei Bureau of Geology and Mineral Exploration and Development), Langfang 065000, China
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Abstract Soil dry density directly influences the mechanical properties of compacted soil. To explore a hyperspectral-based detection method for compacted soil dry density, this study thoroughly analyzed the influence patterns of soil moisture content and dry density on soil spectra. A spectral dewatering method was proposed to improve the accuracy of soil dry density estimation. This study obtained relevant parameters and corresponding spectral data through integrated soil moisture gradient experiments, soil static compaction tests, and soil spectral measurements. By combining spectral processing analysis methods and correlation analysis algorithms, the spectral response characteristics of soil moisture content and dry density were analyzed, leading to the proposal of the spectral dewatering method. Subsequently, optimal feature bands were screened and extracted using correlation analysis algorithms and optimal subset construction algorithms. A soil dry density estimation model was constructed using partial least squares regression. Key findings include: (1) Soil moisture content is the primary factor influencing compacted soil spectra, with dry density being a secondary factor; both significantly affect the overall compacted soil spectral signature. (2) Compared to raw spectra and soil compaction coefficients, spectra corrected by the Spectral Dewatering method exhibit significantly higher sensitivity to soil dry density. The maximum correlation coefficient R reached 0.858, with an average correlation coefficient improvement of 33.7% (wavelet transform). This indicates that the spectral dewatering method employed in this study effectively mitigates moisture's influence on soil spectra and enhances spectral sensitivity to soil dry density. (3) Compared to the soil compaction coefficient, the model constructed based on SD showed an average increase of 3.36% in R2 and an average decrease of 9.985% in RMSE. The optimal model (7 scales) constructed using the Spectral Dewatering method achieved R2=0.792 and RMSE=0.184, demonstrating that the proposed Spectral Dewatering technique further enhances the ability of spectral data to estimate soil dry density. The conclusions drawn in this study provide fundamental theoretical and methodological support for rapid, non-destructive monitoring of soil dry density in engineering foundations.
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Received: 2025-03-06
Accepted: 2025-10-11
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Corresponding Authors:
LI Xiao-fang
E-mail: lixiaofang@lfnu.edu.cn
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