As the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to monitor the spatial and temporal changes of Beijing’s impervious surfaces. This classification strategy achieved excellent performance in the impervious surface extraction in complex urban areas, as the Kappa coefficient reached 0.850. Based on this strategy, the impervious surfaces inside Beijing’s sixth ring road in 1997, 2002, 2007, 2013, and 2017 were extracted. As the development of Beijing has a special regional feature, the changes of impervious surfaces within the sixth ring road were assessed. The findings are as follows: (1) the textural features can significantly improve the classification accuracy of land cover in urban areas, especially for the impervious surface with high albedo. (2) Impervious surfaces within the sixth ring road expanded dramatically from 1997 to 2017, had three expanding periods: 1997–2002, 2002–2007, and 2013–2017, and only shrank in 2007–2013. There are different possible major driving factors for each period. (3) The region between the fifth and sixth ring roads in Beijing underwent the most significant changes in the two decades. (4) The inner three regions are relatively highly urbanized areas compared to the outer two regions. Urbanization processes in the interior regions tend to be completed compared to the exterior regions.

The Chang’e-1/2 satellites carried microwave radiometers (MRM), which supplied unique passive microwave data, supplementing visible, thermal infrared, and radar data in current lunar studies. However, the application of MRM data is constrained by the limited amount of the original dataset, which cannot express spatial variations at given local times. In this study, we present a novel approach to constructing a local-time brightness temperature (TB) model using barycentric interpolation based on Delaunay tetrahedralization. This model enables the generation of a complete TB dataset, producing TB maps that are both continuous and self-consistent in both spatial and temporal domains. Compared to the traditional methods, those generated in this work avoid a ~7-K bias on a global scale. Furthermore, the interpolated maps offer superior representations of the TB over time for various lunar surfaces. The preliminary evaluations on global and regional scales hint important application of MRM data in studying the geological features of the Moon.

Monitoring the surface deformation is of great significance, in order to explore the activity and geophysical features of the underground deep pressure source in the volcanic regions. In this study, the time series surface deformation of the Changbaishan volcano is retrieved via Sentinel-1B SAR data, using the SBAS-InSAR method. The main results are as follows. (1) The mean surface deformation velocity in the Changbaishan volcano is uplifted as a whole, while the uplift is locally distributed, which shows a strong correlation with faults. (2) The time series surface deformation of the Changbaishan volcano indicates an apparently seasonal change. (3) The cumulative surface deformation shows a strong correlation with the maximal magnitude and number of annual earthquakes, and it is likely dominated by the maximal magnitude of the annual earthquakes. (4) The single Mogi source model is appropriate to evaluate the deep pressure source in the Changbaishan volcano, constrained by the calculated surface deformation. The optimal estimated depth of the magma chamber is about 6.2 km, and the volume is increased by about 3.2 × 10⁶ m³. According to the time series surface deformation, it is concluded that the tectonic activity and faults, related to the deep pressure source, are pretty active in the Changbaishan volcano.

Lunar cryptomare records both early-stage mare volcanisms and large-scale impact cratering, which can provide important information about the thermal evolution of the Moon. We built a mixing dielectric constant model to represent the cryptomare deposits mixed by highland debris and mare deposits, and the proper radiative transfer simulation was constructed to evaluate the thermal emission features of surface deposits in the cryptomare region. The microwave radiometer (MRM) data in the Balmer-Kapteyn region were extracted, and the linear interpolation method was used to generate brightness temperature (TB) maps at noon and at night. To enhance the correlation between cryptomare deposits and TB performances, normalized TB (nTB) and TB difference (dTB) maps were also generated. Combined with the datasets, including Lunar Reconnaissance Orbiter Wide Angle Camera, Lunar Orbiter Laser Altimeter, and Diviner and Clementine UV–VIS, the main findings are as follows: (1) The mare-like cryptomare deposits were discovered and identified according to the nTB and dTB performances. Combined with the surface compositions, at least two kinds of buried mare deposits were identified in the B-K region, which erupted during different episodes. (2) A construct-like volcanic feature was suggested by the nTB and dTB performances. (3) The results of our analysis indicated the presence of materials with low dTB anomalies in the northern and southwestern parts of the cryptomare region and in the mare unit within the Vendelinus crater, which illustrates the heterogeneity of the lunar crust in the vertical direction.

Marius Hills volcanic complex (MHVC) comprises one of the densest volcanic structures on the Moon and is of significant interest for comprehending lunar volcanism. In this study, brightness temperature (TB), normalized brightness temperature (nTB), and brightness temperature difference (dTB) were first generated based on microwave radiometer (MRM) data from the ChangE-2 Lunar Orbiter and (FeO+TiO2) abundance was calculated from Clementine UV-VIS data to investigate the microwave thermal emissions and evolutionary history of the MHVC. The results are as follows. Firstly, surface composition derived from the optical data is strongly influenced by impact events, and MRM data can better reflect the thermophysical properties of surface deposits. Secondly, a notable concentration of high TB values at daytime suggests an enrichment of volcanic activities. Thirdly, later volcanic activities are indicated by the TB performances at daytime and night in multiple regions. Finally, a hot anomaly was detected by MRM data, suggesting the higher substrate temperature and the existence of a potential hidden volcanic structure. The results suggest that MRM data provide new insights into the evolutionary history of the MHVC, as well as an important perspective for lunar thermal evolution.

Change-5 (CE-5) mission was successful to return samples up to 1.8 kg from the northeastern part of the Rmker region, Moon. These samples can provide new understandings for the basaltic volcanism of northern Oceanus Prcellarum. Therefore, the terrain-related analysis of the Rmker region will become a hotspot in the recent and latter studies. As a typical surface analyzing method, multi-scale surface roughness has been used in planetary studies for a long time. However, fewer studies of topographic roughness analysis are applied in the Rmker region. The visual interpretations directly from digital elevation models (DEMs) or images potentially cause controversial results. To provide a way for re-understanding the geomorphological features in Rmker region, we investigate a new roughness analyzing method to extract the topographic signatures by using a wavelet-based V-system of degree 1. Based on these signatures, the hectometer- and kilometer-scale roughness are calculated and mapped. The multi-scale roughness textural changes are applied to provide new understandings of the geomorphological features of Rmker region. Our results demonstrate that: (1) most of younger mare units show lower roughness textures following with the scale increases. Especially at kilometer scales, a new understanding is given to units Im1 and Em1 based on the statistical variations of the geologic units; (2) Compared with the results obtained from the optical data, roughness maps provide a new way to recognize the wrinkle ridges in the Rmker region. The wrinkle ridges are re-evaluated based on the hectometer- and kilometer-scale roughness textural changes.