  This study introduces a data-driven approach using machine learning (ML)
techniques to explore and predict albedo anomalies on the Moon's surface. The
research leverages diverse planetary datasets, including
high-spatial-resolution albedo maps and element maps (LPFe, LPK, LPTh, LPTi)
derived from laser and gamma-ray measurements. The primary objective is to
identify relationships between chemical elements and albedo, thereby expanding
our understanding of planetary surfaces and offering predictive capabilities
for areas with incomplete datasets. To bridge the gap in resolution between the
albedo and element maps, we employ Gaussian blurring techniques, including an
innovative adaptive Gaussian blur. Our methodology culminates in the deployment
of an Extreme Gradient Boosting Regression Model, optimized to predict full
albedo based on elemental composition. Furthermore, we present an interactive
analytical tool to visualize prediction errors, delineating their spatial and
chemical characteristics. The findings not only pave the way for a more
comprehensive understanding of the Moon's surface but also provide a framework
for similar studies on other celestial bodies.
