为从土壤光谱中提取土壤有机质的光谱响应信息, 提升土壤有机质含量诊断精度与可靠性, 以潮土有机质含量为研究对象, 以北京市区域的96个耕层土壤参数与高光谱数据为数据源开展研究分析; 先采用二进制小波技术将土壤光谱数据分离为5个尺度的高频数据与低频数据, 再将低频数据、 高频数据分别与土壤有机质实测数据进行相关性分析, 提取最佳波段组合, 构建有机质含量诊断模型。 结果表明: （1）二进制小波技术可抑制噪声对高频信息的干扰, 能有效提升光谱对土壤有机质含量的敏感性, 进而提升有机质含量的诊断精度与可靠性; （2）在二进制小波技术下, 高频信息对有机质含量的诊断能力明显优于低频信息, 低频信息对土壤有机质含量的诊断能力随尺度增加而降低, 高频信息随尺度增加呈先提升而后降低的趋势; （3）与数学方法相比, 基于二进制小波变换算法构建的模型精度较高, 稳定性较好, 其最优模型的预测精度提高了315%, 可靠性增加了105%。

In order to separate the information of the content of soil organic matter contained in soil spectra, to extract the spectral response information of the matter, to improve the diagnostic accuracy and reliability of soil organic matter content, this study takes the content of organic matter in tidal soil as the research object, and takes the soil parameters and hyperspectral data of 96 farmlands collected from Beijing area as the data source to research and analyze. First, the binary wavelet technique is used to separate the soil spectral data into 5 scales of high-frequency data and low-frequency data, and then these two kinds of data are respectively used for the correlation analysis with the measured soil organic matter data. Afterwards, the optimal band combination is extracted to build the diagnosis model of organic matter content. Finally, results of the study show that: (1) The binary wavelet technology can restrain the noise interference to high frequency information, and effectively enhance the spectral sensitivity to soil organic matter content so as to improve the diagnostic accuracy and reliability of organic matter content; (2) Under the binary wavelet technique, the diagnostic ability of high frequency information to organic matter content is obviously superior to that of low frequency information. The diagnostic ability of low frequency information to soil organic matter content decreases with the increase of scale, while high frequency information increases with the scale increasing and then decreases; (3) Compared with the mathematical method, the model based on the binary wavelet transform algorithm has higher accuracy and better stability. The prediction accuracy of the optimal model is improved by 315% and the reliability is increased by 105%.