[Quantitative inversion of rock SiO2 content based on thermal infrared emissivity spectrum].

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 microm 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.

[Quantitative estimation of CaO content in surface rocks using hyperspectral thermal infrared emissivity].

The objective of the present paper is to study the quantitative relationship between the CaO content and the thermal infrared emissivity spectra. The surface spectral emissivity of 23 solid rocks samples were measured in the field and the first derivative of the spectral emissivity was also calculated. Multiple linear regression (MLR), principal component analysis (PCR) and partial least squares regression (PLSR) were modeled and the regression results were compared. The results show that there is a good relationship between CaO content and thermal emissivity spectra features; emissivities become lower when CaO content increases in the 10.3-13 mm region; the first derivative spectra have a better predictive ability compared to the original emissivity spectra.