Abstract: Taking organic matter contents of 120 soil samples and corresponding spectral data in Tuoketuo County as data sources, the feasibilities of hyperspectral inversion of soil organic matters under different type of soils and lands  of different use were explored. The original spectrum (R), spectral reciprocal (1/R), spectral logarithm (LnR) and spectral first-order differential (R′) were decomposed by continuous wavelet transform to generate wavelet coefficients, and the correlation between soil organic matter and wavelet coefficients was analyzed, and BP neural network and support vector machine (SVM) were established by extracting the characteristic bands. The results were followed. ①The correlation coefficients between R, 1/R, LnR, R′ and soil organic matter were increased by 0.204, 0.090, 0.199 and 0.252 after continuous wavelet transform, respectively, which showed that continuous wavelet transform could deeply mine the potentially spectral information and enhance the correlation with organic matter content. ② Before continuous wavelet processing, SVM could not predict the content of soil organic matter, while after processing, the accuracies (R2) of SVM-CWT-R and SVM-CWT-R′ were 0.50 and 0.56, Root mean square errors (RMSE) were 0.17 and 0.15, residual predictive deviations (RPD) were 1.62 and 1.53, respectively, which realized the effective estimation of SOM. ③ After continuous wavelet transform, the results of BP neural network prediction model were improved. Among them, BP-CWT-LnR prediction model had the best effect, R2 was 0.76, which was higher than BP-lnR, RMSE was 0.15 reduced by 0.04, RPD was 2.12 increased by 0.87. Therefore, the BP-CWT-LnR hyperspectral inversion model could provide theoretical reference and technical support for precision agriculture.

Key words: continuous wavelet transform, BP neural network, support vector machine, precision agriculture

摘要： 以托克托县境内120个土壤有机质含量以及对应光谱数据为数据源，探究了不同土壤类型与土地利用类型下土壤有机质高光谱反演研究的可行性，采用连续小波变换对原始光谱（R）、光谱倒数（1/R）、光谱对数（LnR）、光谱一阶微分（R′）进行分解生成小波系数并与土壤有机质进行相关系分析，提取特征波段建立BP神经网络与支持向量机模型（SVM）。结果表明：①R、1/R、LnR、R′与土壤有机质相关系数经过连续小波变换后，较之前增加了0.204、0.090、0.199、0.252，表明连续小波变换可深度挖掘光谱潜在信息，提升与有机质含量之间的相关系数。②未经过连续小波处理前，SVM无法实现对当地土壤有机质含量的预测，经过处理后，模型SVM-CWT-R与SVM-CWT-R′的精度决定系数分别达到了050、0.56，均方根误差为0.17、0.15，相对分析误差为1.62、1.53，实现了对土壤有机质的有效估算。③经过连续小波变换后BP神经网络预测模型结果得到提升，其中BP-CWT-LnR预测模型效果最佳，精度决定系数达到0.76，较之前BP-LnR提升了0.2；均方根误差达到015，降低0.04；相对分析误差为2.12，增加了0.87。因此利用BP-CWT-LnR高光谱反演模型进行区域土壤有机质遥感监测，可为当今精准农业提供理论参考与技术支持。

关键词: 连续小波变换, BP神经网络, 支持向量机, 精准农业