This study shows that a supersonic moon shadow of a total solar eclipse can steepen the ionospheric total electron content (TEC) wave on August 21, 2017. A data‐adaptive method named Hilbert‐Huang transform is employed to examine the nonlinear and non‐stationary evolution of the waves. The results show that the TEC wave behaves as a traveling ionospheric disturbance before the totality appearance, turns later into steepening, and breaks eventually. A TEC wave with a period of ∼40 min and wavelength of ∼1,000 km propagates mainly in an east‐southward direction before the totality appearance. The wave amplitude and scales, respectively, increases and reduce by near ∼50% as the moon shadow approaches the western coast of the continental United States. The short‐period TEC waves (period ∼2 min) reveal that the wave may break eventually when the wave gets steeper. The steepness of the TEC wave is reconstructed according to the constructive interference.

In the day time of the Moon surface, the strong illumination from high altitude and high albedo rate radical craters will introduce the illumination effect on observing the nearby low altitude, low albedo rate and shallow small slop rate area seriously, and even can “hide” the later area from the light. Based on the lunar global topography model obtained by Chang’E-1 mission, and by comparing with the lunar gravity model, a volcano named “YUTU Mountain” has been identified. It is a volcano with diameter of ∼300 km and height of ∼2 km located at (14°N, 308°E) in Oceanus Procellarum. Besides, the DEM of another volcano named “GUISHU Mountain” in the same area has been improved. This new discovery will benefit the study of lunar magmatism and volcanism evolution in the nearside of the Moon.

Lunar volcanic domes are essential windows into the local magmatic activities on the Moon. Classification of domes is a useful way to figure out the relationship between dome appearances and formation processes. Previous studies of dome classification were manually or semi-automatically carried out either qualitatively or quantitively. We applied an unsupervised machine-learning method to domes that are annularly or radially distributed around Gardner, a unique central-vent volcano located in the northern part of the Mare Tranquillitatis. High-resolution lunar imaging and spectral data were used to extract morphometric and spectral properties of domes in both the Gardner volcano and its surrounding region in the Mare Tranquillitatis. An integrated robust Fuzzy C-Means clustering algorithm was performed on 120 combinations of five morphometric (diameter, area, height, surface volume, and slope) and two elemental features (FeO and TiO₂ contents) to find the optimum combination. Rheological features of domes and their dike formation parameters were calculated for dome-forming lava explanations. Results show that diameter, area, surface volume, and slope are the selected optimum features for dome clustering. 54 studied domes can be grouped into four dome clusters (DC1 to DC4). DC1 domes are relatively small, steep, and close to the Gardner volcano, with forming lavas of high viscosities and low effusion rates, representing the latest Eratosthenian dome formation stage of the Gardner volcano. Domes of DC2 to DC4 are relatively large, smooth, and widely distributed, with forming lavas of low viscosities and high effusion rates, representing magmatic activities varying from Imbrian to Eratosthenian in the northern Mare Tranquillitatis. The integrated algorithm provides a new and independent way to figure out the representative properties of lunar domes and helps us further clarify the relationship between dome clusters and local magma activities of the Moon.