High-Resolution Lunar Brightness Temperature Model Based on Chang'e-2 MRM Data and Spatially Weighted Neural Network

Brightness temperature (TB) derived from micro- wave radiometers (MRMs) onboard China's Chang'e (CE) satellites has provided significant insights into the Moon's subsurface thermal conditions and evolution. However, conventional TB mapping techniques emphasize spatial correlations among observational data points while largely neglecting the influence of inherent lunar surface factors. In this study, we propose a novel TB estimation approach utilizing geographically neural network weighted regression (GNNWR) combined with multisource lunar remote sensing data to generate TB maps at a higher spatial resolution of 0.0625(degrees )x 0.0625(degrees). This method integrates crucial lunar surface parameters in heat conduction and radiation transfer models in a new framework, thereby reducing the risk of overestimation associated with high-resolution targets in sparsely distributed samples. In addition, by replacing the traditional geographically weighted regression (GWR) kernel with a spatially weighted neural network (SWNN), the model effectively addresses spatial nonstationarity and heterogeneity present in TB data and microwave radiative transfer. Comparative analyses demonstrate that the GNNWR approach achieves superior performance, as evidenced by the highest R-2 and the lowest mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). Furthermore, the generated TB maps demonstrate strong alignment with observed spatial trends. These maps also reveal fine-scale thermal features typically obscured by conventional interpolation methods, enhancing their utility for microwave thermal emission analysis and geological studies.