利用野外测量21种地表岩石的高光谱发射率数据， 进行包络线去除和归一化处理后， 运用逐步回归法进行波段选择， 分析了SiO2含量与包络线去除后特征波段发射率的定量关系。 在此基础上， 通过比较12种SiO2光谱指数， 建立了定量反演SiO2含量的最优模型。 结果表明： 构建的SiO2光谱指数能有效预测SiO2含量， 其中基于11.18和12.36　μm波段的归一化SiO2光谱指数(normalization silicon dioxide index， NSDI)的预测能力最高； 与回归模型相比， 光谱指数更简单、 实用； 研究结果在岩石种类鉴定及SiO2含量的高精度提取方面有重要应用价值。

The present paper used the emissivity of non-processed rocks measured by M304, a hyperspectral Fourier transform infrared (FTIR) spectroradiometer, and SiO2 content by the X-ray fluorescence spectrometry. After continuum removal and normalization, stepwise regress method was employed to select the feature bands of rocks emissivity. And then quantitative relationship between SiO2 content and continuum removal emissivity of feature bands was analysed. Based on that, by comparing twelve SiO2 indices models, the optimal model for predicting SiO2 content was built. The result showed that the SiO2 indices can predict SiO2 content efficiently, and especially the normalization silicon dioxide index (NSDI) about 11.18 and 12.36 μm is the best; compared with regression models, NSDI is simpler and has higher practicality; the result has an important application value in rock classification and SiO2 content extraction with high precision.