基于离散小波的土壤全氮高光谱特征提取与反演

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 Li-Ln , 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. ---- 土壤全氮是重要的养分指标, 利用高光谱技术研究并构建砂姜黑土全氮含量高光谱估测模型, 为作物施肥及发展精确农业提供参考. 尝试研究离散小波估测土壤全氮含量的可行性, 以河南省商水县不同小麦氮肥处理为试验区, 采集 100 份 0~20 cm 的砂姜黑土, 土壤样本风干并经研磨过筛等处理后, 在实验室暗室内采集光谱. 利用含量梯度法, 将总样本(100 个砂姜黑土) 划分为建模集 75 个和验证集 25 个. 将原始光谱进行一阶导数变换, 并对一阶导数光谱分别进行相关分析和离散小波变换, 同时结合支持向量机和 K 邻近算法构建高光谱土壤全氮估测模型. 系统分析了原始光谱和 阶导数光谱的单波段与土壤全氮的相关性, 结果表明, 经 阶导数变换后的光谱与土壤全氮有更好的相关性, 在 1 373 nm 处相关系数达到最高为 0.84. 利用离散小波算法对一阶导数光谱进行最佳母小波和分解层次选择*结果显示, 经 sym8 函数分解的小波系数能较好的重构土壤全氮光谱信息, 进 步基于分解层 L1—L11 的低频系数分别建立支持向量回归和 K 邻近回归土壤全氮含量估测模型, 比较全部估测模型, 以分解层 L5 的低频系数结合 K 邻近构建的模型最优, 建模决定系数为 0.90, 均方根偏差为 0.09g·kg-1, 相对分析误差为 3.78, 验证决定系数为 0.97, 均方根偏差为 0.05 g·kg-1, 相对分析误差为 4.30. 同时与全波段和经相关分析后挑选出的敏感波段作为输入构建的模型进行比较, K 邻近模型精度提高了 3.2% 和 9%, 支持向量机模型精度提高了 6.7% 和 11.6%.研究结果表明一阶导数变换与离散小波技术可有效减少噪声影响, 提高土壤全氮含量的估测精度, 又实现了光谱数据降维, 简化了模型复杂度, 为砂浆黑土全氮含量的精确估测提供参考.