| Hyperspectral remote sensing(400～2500nm) prompt remote sensing technologyto develop from ground matter discrimination to identification. In remote sensinggeology, the classes and reliability of mineral identified have been improved.However, the rock-forming minerals containing no cation-OH bond can not beidentified because this spectral band can only detect some minerals containingcation-OH bond. One of approach to detect minerals containing no cation-OH bond isto use thermal remote sensing technology, which can detect the Si-O bond ofrock-forming minerals. Therefore, combining use of hyperspectral(400～2500nm)and thermal remote sensing will improve the ability and precision of mineralsidentification by remote sensing. However, minerals information extraction based onemissivity spectral features has lagged behind hyperspectral remote sensing, becauseit is difficult to obtain and process thermal remote sensing data as well as emissivityderivation.In this paper, mechanisms of emissivity of minerals (determinant and variationfactors of emissivity), atmospheric correction and emissivity derivation, and methodof minerals identification based on emissivity spectral features are studied.Emissivity spectral of several minerals have been calculated using mineralradiative transfer model and compared with measured spectral in ASU spectraldatabase (Arizona State University). The comparison indicates that Mie/Hapke modelcan simulate emissivity spectral features of minerals. It is studied using Mie/Hapkemodel that mineral emissivity spectral features variate with the mineral granularityand emission angle, and results indicate that the law of emissivity variation withgranularity is totally different from mineral to mineral. Along with the variation ofmineral granularity, the shape and absorption depth of mineral emissivity spectral willall variate. However, the law of emissivity variation with emission angle of differentminerals are identical. Along with the increasement of emission angle, emissivitydecrease. The more emissivity is small, the more variation range and speed are large.The reflectance mixture of mineral is non-linear, and can be lineated using mineralradiative transfer model. After the mixture spectral is lineated, the precision of linearunmixing of spectral and mineral content extraction will be improved greatly. Mineralradiative transfer model can model the mechanisms of spectral formation andvariation, and is one of study method of spectral mechanism.The atmospheric correction and emissivity derivation of thermal remote sensingdata will affect application greatly. In this paper, it is quantitatively evaluated howatmospheric profiles affect the atmospheric correction and relative emissivityderivation. Based on the study results, method of atmospheric correction and relativeemissivity derivation is developed without atmospheric profiles. The derivation resultof ASTER thermal infrared data using this method is identical with the standardproduct.SiO2 content of rock is retrieved quantitatively using ASTER thermal infrareddata, and the derivation method can be used to explorate ore deposit concerning withSiO2 of rock, such as copper-nickel deposit exist in basic-ultrabasic intrusive bodyand gold deposit forming in rocks containing high-Si content. This method also can beused in study the degeneration of soil. The content and distribution of several mineralssuch as muscovite, sulphate, and feldspar, hematite, calcite, dolomite and pyroxeneare derived using TES(Thermal Emission Spectrometer) data of Mars, and thisresearch result has important significance in study the formation and evolvement ofMars.It is indicates that thermal remote sensing has some advantages over reflectanceremote sening(400～2500nm) in some aspects. Mechanisms of emissivity of minerals,data processing and information extraction are studied, and based on study resultsmineral identification regulation of complete spectrum can be developed. It is one ofapproach improving precision and reliability of mineral identification to syntheticallyuse thermal remote sensing and reflectance remote sensing(400～2500nm).
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