1. 河南农业大学信息与管理科学学院，河南 郑州 450002
2. 河南粮食作物协同创新中心，河南 郑州 450002
3. Universidade Nova de Lisboa, NOVA Informantion Managment School, Lisboa, 1070-312, Portugal
Hyperspectral Feature Extraction and Estimation of Soil Total Nitrogen Based on Discrete Wavelet Transform
ZHANG Juan-juan1, 2, NIU Zhen1, 2, MA Xin-ming1, 2, WANG Jian1, XU Chao-yue1, 2, SHI Lei1, 2, Bação Fernando3, SI Hai-ping1, 2*
1. Henan Agricultural University, College of Information and Management Science, Zhengzhou 450002, China
2. Henan Agricultural University, Collaborative Innovation Center of Henan Grain Crops, Zhengzhou 450002, China
3. Universidade Nova de Lisboa, NOVA Information Management School, Lisboa, 1070-312, Portugal
Abstract：Soil total nitrogen is an important nutrient index. Hyperspectral technology is used to study and build a hyperspectral estimation model of total nitrogen content in Shajiang black soil, which provides a reference for crop fertilization and the development of precision agriculture. This paper attempts to study the feasibility of discrete wavelets to estimate soil total nitrogen content. Taking different wheat nitrogen fertilizer treatments in Shangshui County, Henan Province, as the experimental area, 100 samples of Shajiang black soil with a depth of 0~20 cm were collected. After the soil samples were air-dried in the dark and processed by grinding and screening, the spectra were collected in the darkroom of the laboratory. The total samples (100 sand ginger black soil) were divided into 75 modeling sets and 25 validation sets. The original spectrum was transformed by the first derivative, and the first derivative spectrum was analyzed by correlation analysis and discrete wavelet transform respectively. At the same time, the hyperspectral estimation model of soil total nitrogen content was constructed by combining the support vector machine and the k-nearest neighbor algorithm. The correlation between the single band of the original spectrum and the first derivative spectrum and soil total nitrogen were systematically analyzed. The results showed that after the first derivative transformation, the spectrum had a better correlation with soil total nitrogen, and the correlation coefficient reached 0.84 at 1 373 nm. The discrete wavelet algorithm selects the best mother wavelet and decomposition level of the first derivative spectrum. The results show that the wavelet coefficients decomposed by the Sym8 function can better reconstruct the spectral information of soil total nitrogen. Further, based on the low-frequency coefficients of decomposition layer L1—L11, the support vector regression and k-nearest neighbor regression estimation models of soil total nitrogen content were established respectively, and all the estimation models were compared. The model constructed by combining the low-frequency coefficients of decomposition layer L5 with k-nearest neighbor is the best. The determination coefficient of modeling is 0.90, the root mean square deviation is 0.09 g·kg-1, and the relative analysis error is 3.78. The validation determination coefficient is 0.97, the root mean square deviation is 0.05 g·kg-1, and the relative analysis deviation is 4.30. At the same time, compared with the model constructed with the full band and the sensitive band selected after correlation analysis as input, the accuracy of the K-neighbor model is improved by 3.2% and 9%, and the accuracy of the support vector machine model is improved by 6.7% and 11.6%. The results show that the first derivative transform and discrete wavelet technology can effectively suppress the impact of noise, improve the estimation accuracy of soil total nitrogen content, reduce the dimension of spectral data, simplify the complexity of the model, and provide a reference for the accurate estimation of the total nitrogen content of Shajiang black soil.
Key words：Shajiang black soil; Total nitrogen; Hyperspectral; Discrete wavelet; K-neighbor
张娟娟，牛 圳，马新明，王 健，徐超越，时 雷，Bação Fernando，司海平. 基于离散小波的土壤全氮高光谱特征提取与反演[J]. 光谱学与光谱分析, 2023, 43(10): 3223-3229.
ZHANG Juan-juan, NIU Zhen, MA Xin-ming, WANG Jian, XU Chao-yue, SHI Lei, Bação Fernando, SI Hai-ping. Hyperspectral Feature Extraction and Estimation of Soil Total Nitrogen Based on Discrete Wavelet Transform. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3223-3229.
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