Simulation of brightness temperature from lunar surface and inversion of regolith-layer thickness

A correspondence of the lunar regolith-layer thickness to the lunar digital elevation mapping is proposed to tentatively construct the global distribution of lunar regolith-layer thickness. Using Clementine ultraviolet visible multispectral data, the global spatial distribution of FeO + TiO2 content on the lunar regolith layer is calculated. Thus the dielectric permittivity of the global lunar regolith layer can be obtained on the basis of the relationship between dielectric permittivity, bulk density, and FeO + TiO2 content. On the basis of some measurements of physical temperature of the lunar surface, an empirical formula of physical temperature distribution over the lunar surface is presented. On the basis of aforementioned works, brightness temperature of the lunar regolith layer in passive microwave remote sensing, which is planned for the Chinese Chang-E lunar project, is numerically simulated by a three-layer model ( the layering dust, regolith, and underlying rock media) using fluctuation dissipation theorem. Taking these simulations with random noise as observations, an inversion approach of the lunar regolith-layer thickness is developed. Because the penetration depth is small for the areas with high FeO + TiO2 content and at high-frequency channels, for example, 19.35 GHz and 37.0 GHz in Chang-E project, the physical temperature of the top dust layer and the regolith layer can be inverted by the brightness temperature at these high-frequency channels using a two-layer model. Making statistics from those points with high FeO + TiO2 content along each latitude as the reference points, the temperature variation with latitude can be retrieved. Then the regolith-layer thickness can be inverted by brightness temperature at lower-frequency channels, such as 1.4 GHz or 3.0 GHz. Numerical simulation and the inversion approach make an evaluation of the performance for lunar passive microwave remote sensing and for future data calibration and validation.