Brightness temperature (TB) data from the Chinese Chang’E-2 (CE-2) microwave radiometer (MRM) are constrained by the limited quantity of the original dataset, which cannot express global TB distribution. In order to construct the lunar TB model with the TB data obtained by the MRM on board CE-2, we propose a novel spherical geometric B-spline (SGB-spline) model. The model fully integrates the observed TB data with the lunar geometric features and determines optimal fitting parameters through a subdivision-based optimization process. More specifically, the establishment of the lunar TB model begins by employing spherical area coordinates (SACs) for CE-2 TB data representation across all four frequency channels, followed by applying geometric B-splines to refine the TB distribution. At the same time, it preserves the geometric integrity of the Moon. We observed that the SGB-spline model constructs the more comprehensive TB models during both lunar daytime and nighttime in the 3D Euclidean space, providing a more detailed representation of the spherical spatial information and the effect of frequency channels. Experimental results demonstrated that the proposed SGB-spline model significantly outperforms representative interpolation approaches.

The Chang’e-2 (CE-2) microwave radiometer (MRM) brightness temperature (TB) observations have significantly enhanced our understanding of lunar subsurface thermal features. While existing TB mapping approaches rely heavily on interpolation methods, this study proposes an advanced framework that combines heat conduction with multilayer microwave radiative transfer to generate continuous TB distributions over 24 hours. The proposed approach incorporates key lunar thermophysical parameters to ensure physical consistency in TB simulations. A spatiotemporal integration and validation scheme was also proposed to enhance data quality. Systematic biases between observed and modeled TB values were then addressed through a calibration approach using sine functions. The simulated TBs align well with the CE-2 MRM observations at both 19.35 GHz and 37.0 GHz. The approach accurately captures global TB patterns while preserving local thermal signatures across diverse geologic units. Detailed analyses at CE landing sites further validate the model’s capability to capture fine-scale thermal variations. The improved spatiotemporal coverage and accuracy of these TB maps provide valuable insights into lunar thermal and geological processes.

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$0.0625^{\circ } \times 0.0625^{\circ }$. 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.

The regional analysis of lunar brightness temperature (TB) data by the microwave radiometer (MRM) onboard the Chang’E-2 (CE-2) lunar probe holds significant importance in the field of thermal characteristics and TB distribution. However, most existing methods rely on a 2-D conversion, which makes it challenging to represent spherical TB data directly due to the nondevelopability of the sphere. Inspired by the triangular domain, we propose a hierarchical spherical algorithm for lunar regional TB analysis, which embeds the spherical triangular domain into the interpolation. First, we construct an octahedral framework based on spherical area coordinates (SACs), which obtain spherical information over the triangular domain by establishing coordinate correlation to enhance the feature representation ability of TB data. Second, we propose a spherical triangulation approach to reduce the loss of detailed TB information in the lunar regions. Finally, the hierarchical spherical algorithm refines the spherical triangular lattices from coarse to fine in the lunar regions. It integrates all the spherical TB functions from each level of the hierarchy into the 3-D features on the sphere, minimizing the result errors. The experimental results suggest that the hierarchical spherical algorithm for lunar regional TB construction has competitive performance and outperforms several representative methods. Extensive experiments are carried out to analyze the diurnal TB variations in typical regions, i.e., the Copernicus, Tycho, Aristarchus, and Stevinus.

With the continuous advancements in the Chinese lunar exploration program, the substantial brightness temperature (TB) data acquired by the Chang’e (CE) orbiter series have provided unprecedented opportunities for studying the geology of the Moon. In particular, the TB data obtained by the CE-2 mission have offered a new observational perspective for investigating the geological characteristics of the Moon. In this article, we propose a novel triangulated spherical technique for constructing a TB model of the Moon with CE-2 microwave radiometer (MRM) data. Specifically, we directly parameterize spherical TB data based on spherical area coordinates and use spherical Bézier surfaces to capture the TB distributions for daytime and nighttime across different frequency channels. This model is enabled by the theoretical characterization of spherical data, which allows us to construct geometric formulations that parameterize the entire TB data of the Moon. It incorporates an octahedral partitioning strategy and divides the lunar surface into eight regions, each undergoing iterative refinement to enhance precision through detailed analysis. Furthermore, the spherical Bézier surfaces effectively mitigate the complexity of structural components on TB estimations. Experimental results demonstrate that the proposed TB model of the Moon significantly outperforms existing approaches in characterizing the lunar TB distribution, providing important application in analyzing the geological features and thermal processes of the Moon.