The anthropogenic nighttime light (NTL) data that are acquired by satellites can characterize the intensity of human activities on the ground. It has been widely used in urban development assessment, socioeconomic estimate, and other applications. However, currently, the two main sensors, Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), provide inconsistent data. Hence, the application of NTL for long-term analysis is hampered. This study constructed a new inter-calibration method for DMSP-OLS and NPP-VIIRS nighttime light to solve this problem. First, NTL data were processed to obtain vicarious site across China. By comparing different candidate models, it is discovered the Biphasic Dose Response (BiDoseResp) model, which is a weighted combination of sigmoid functions, can best perform the regression between DMSP-OLS and logarithmically transformed NPP-VIIRS. The coefficient of determination of BiDoseResp model reaches 0.967. It’s residual sum of squares is $6.136\times {10}^5$ , which is less than $6.199\times {10}^5$ of Logistic function. After obtaining the BiDoseResp-calibrated VIIRS (BDRVIIRS), we smoothed it by a filter with optimal parameters to maximize the consistency. The result shows that the consistency of NTL data is greatly enhanced after calibration. In 2013, the correlation coefficient between DMSP-OLS and original NPP-VIIRS data in the China region is only 0.621, while that reaches to 0.949 after calibration. Finally, a consistent NTL dataset of China from 1992 to 2018 was produced. When compared with the existing methods, our method is applicable to the full dynamic range of DMSP-OLS. Besides, it is more suitable for country or larger scale areas. It is expected that this method can greatly facilitate the development of research that is based on the historical NTL archive.

Earth observation satellites can provide systematic and continuous monitoring of clouds. High spatial resolution (5 km) cloud fraction data are retrieved from the Moderate Resolution Imaging Spectroradiometer cloud mask products of Terra (12 years) and Aqua (9 years) over China. Long-term trends of cloud fraction for morning and afternoon observations clearly reflected seasonal variations. We found more clouds in the afternoon than in the morning. There is a strong correlation between satellite and surface observations of the daily cloud fraction for the period of 2012, with correlation coefficients of 0.678 and 0.7 for morning and afternoon, respectively. However, the analyses of the monthly mean cloud fraction between satellite and surface observations showed a larger discrepancy in the winter. In order to investigate the differences between satellite and ground-based cloud fraction over different underlying surfaces, a statistical test was carried out for six areas. The results indicated statistically significant increased correlations (p < 0.0001) between satellite and ground-based cloud fraction in northern China after removing winter data, especially in Northeastern Forest and Taklimakan Desert, while the correlation coefficients in southern China did not show significant changes.

Carbon emissions caused by the massive consumption of energy have brought enormous pressure on the Chinese government. Accurately and rapidly characterizing the spatiotemporal characteristics of Chinese city-level carbon emissions is crucial for policy decision making. Based on multi-dimensional data, including nighttime light (NTL) data, land use (LU) data, land surface temperature (LST) data, and added-value secondary industry (AVSI) data, a deep neural network ensemble (DNNE) model was built to analyze the nonlinear relationship between multi-dimensional data and province-level carbon emission statistics (CES) data. The city-level carbon emissions data were estimated, and the spatiotemporal characteristics were analyzed. As compared to the energy statistics released by partial cities, the results showed that the DNNE model based on multi-dimensional data could well estimate city-level carbon emissions data. In addition, according to a linear trend analysis and standard deviational ellipse (SDE) analysis of China from 2001 to 2019, we concluded that the spatiotemporal changes in carbon emissions at the city level were in accordance with the development of China’s economy. Furthermore, the results can provide a useful reference for the scientific formulation, implementation, and evaluation of carbon emissions reduction policies.

The rapid socio-economic development of the metropolitan area has led to the continuous deterioration of the ecological environment. This leads to intense competition and conflict between different spatial use types. Spatial conflict research is essential to achieve ecological-economic coordination and high-quality development. However, existing studies lack comprehensive and direct ecological-economic spatial conflicts, especially those on the spatial-temporal evolution and potential drivers of spatial conflict. In this study, we identified the ecological-economic spatial conflicts in the Nanjing metropolitan area in 2010, 2015, and 2020. This study used the random forest to analyze the factors that influenced the change of spatial conflict. Results show that: (1) From 2010 to 2020, the ecological-economic spatial conflict in the Nanjing metropolitan area changed significantly. (2) Land use change has an important effect on spatial conflicts, which are easily triggered by uncontrolled urban expansion, but ecological land can mitigate spatial conflicts. (3) Relevant driving factors of spatial conflicts show multi-level features, so the development of conflict reconciliation countermeasures needs to be tailored to local conditions. This study provides a significant foundation for the high-quality development of the Nanjing metropolitan area and provides a reference for the planning and management of the territorial space.

In this paper, we investigate the effects of shape distribution of aerosol particles on the volumetric scattering properties, as well as the radiance and polarization distributions of skylight, by numerical simulations. The results demonstrate that the shape distribution indeed exerts a significant influence on the skylight degree of linear polarization. The skylight polarization calculated assuming the microscope-measured shape distributions is distinct from that using the inversion-based shape distributions. The significant effects will influence the retrieval of the sphericity of aerosols based on the sun–sky radiometer measurements. Our results suggest that using representative shape distributions obtained by direct microscopic observations of aerosol samples captured in the natural atmosphere has a high potential to improve the retrieval of the aerosol shape parameter.