Rapid urbanization is an important factor leading to the rise in surface temperature. How to effectively reduce the land surface temperature (LST) has become a significant proposition of city planning. For the exploration of LST and the urban heat island (UHI) effect in Zhengzhou, China, the LST was divided into seven grades, and the main driving factors of LST change and their internal relations were discussed by correlation analysis and gray correlation analysis. The results indicated that LST showed an upward trend from 2005 to 2020, and a mutation occurred in 2013. Compared with 2005, the mean value of LST in 2020 increased by 0.92 °C, while the percentage of LST-enhanced areas was 22.77. Furthermore, the spatial pattern of UHI was irregularly distributed, gradually spreading from north to south from 2005 to 2020; it showed a large block distribution in the main city and southeast in 2020, while, in the areas where woodlands were concentrated and in the Yellow River Basin, there was an obvious “cold island” effect. In addition, trend analysis and gray correlation analysis revealed that human factors were positively correlated with LST, which intensified the formation of the UHI effect, and the influence of Albedo on LST showed obvious spatial heterogeneity, while the cooling effect of vegetation water was better than that of topography. The research results can deepen the understanding of the driving mechanism of the UHI effect, as well as provide scientific support for improving the quality of the urban human settlement environment.

With the rapid development of urbanization, various urban problems such as air pollution, urban heat islands, poor ventilation, and so on have followed. Ventilation corridors can improve the internal ventilation conditions of a city and can effectively alleviate these urban problems. Taking Kaifeng as an example of a medium-sized city, this study used meteorological analysis and remote sensing (RS) technology and a geographical information system (GIS) to plan ventilation corridors. Through analysis of the thermal environment, the influential factors of the underlying surface (building density, road length, water length, vegetation coverage) and wind environment in Kaifeng, the compensation space and action space, and the comprehensive values of the ventilation potential in each region were determined. The results show that with the use of the single-window algorithm and Landsat8 data, high-resolution ground temperature can be retrieved. Moreover, although the ventilation potential value of Kaifeng in the northern part is better than that in the southern part, the temperature in the north is higher than that in the south. Through comparison and analysis of the location of the low-temperature zone, the air duct opening of the ventilation corridor can be determined and the source areas of three ecological ventilation corridors identified. According to the ventilation corridor air duct opening, the urban underlying surface and the dominant wind direction of Kaifeng city, two ventilation corridors located on the eastern and western sides of Kaifeng city were planned. This urban ventilation corridor planning method, which is based on temperature difference analysis and determination of the source area of the ventilation corridor, can provide a reference for the construction of an ecologically livable city.

The Yellow River is the second largest river in China, and the provinces and cities affected by the Yellow River are called the Yellow River Basin influence area. The relationship between land use and surface thermal effects in the influence area of the Yellow River Basin from 2000 to 2015 was analyzed using MODIS remote sensing data and an energy balance algorithm. The results showed that: (1) net radiation and latent heat flux both increased, and the high value areas were located in the urban expansion areas and natural and seminatural areas, respectively; (2) net surface energy intake showed a upward trend, and increased as the intensity of human influence increased, indicating that human activities had strengthed the positive trend of net surface energy intake and increased the warming effect; (3) net radiation had a greater impact on surface energy intake than latent heat flux, and this relationship was more obvious in land use types that were greatly affected by human activities. This study emphasizes the difference in energy budgets of different land use types under the influence of human activities. It provides a theoretical basis for judging the climate change trend and urban heat island effect in the influence area of the Yellow River Basin from the perspective of biogeophysics.

Human land use intensity affects the surface energy balance by changing the biogeophysical parameters. This study used Moderate Resolution Imaging Spectroradiometer remote sensing data and surface energy balance algorithms to quantify changes in surface energy budgets corresponding to changes in land use in Beijing from 2000 to 2015. Land use was reclassified by considering land use intensity. The difference in the latent heat flux (LE) and net radiation (R_n) (LE−R_n) expressed the warming or cooling effect. The results showed that: (i) The increasing trend of net longwave radiation in Beijing offset the decreasing trend of net shortwave radiation. The R_n changed slightly, while the LE and LE−R_n showed a significant increase of 0.55 and 0.56 W/(m²∙year), respectively. The findings indicated that considering only radiative forcing, or even R_n, was not enough to measure the impacts of land use change on the energy budget. (ii) The order of R_n, LE, and LE−R_n values from high to low were natural and seminatural areas, cropland, mixed pixel areas, urban expansion areas, and old urban areas. Compared with natural and seminatural areas, the changing LE−R_n trend in the other four land use types decreased with the increase in human impact intensity, indicating that human activities weakened the positive change trend of LE−R_n and increased the warming effect. (iii) Although the temporal trend of LE increased in Beijing from 2000 to 2015, the effect of R_n on LE−R_n was greater than that of LE, especially in the four land use types affected by human activities. The results for surface temperature in various land use types confirmed this point. This study highlights the energy budget differences of various land use types affected by human activities. It makes an important contribution to understanding the urban heat island effect from a biogeophysical perspective.

The sensitivity of the ill-conditioned coefficient matrix (CM) and the size of the analysis volume on the retrieval accuracy in the volume velocity processing (VVP) method are analyzed. By estimating the upper limit of the retrieval error and analyzing the effects of neglected parameters on retrieval accuracy, the simplified wind model is found to decrease the difficulty in solving and stabilizing the retrieval results, even though model errors would be induced by neglecting partial parameters. Strong linear correlation among CM vectors would cause an ill-conditioned matrix when more parameters are selected. By using exact coordinate data and changing the size of the analysis volume, the variation of the condition number indicates that a large volume size decreases the condition number, and the decrease caused by increasing the number of volume gates is larger than that caused by increasing the sector width. Using the spread of errors in the solution, a demonstration using mathematical deduction is provided to explain how a large analysis volume can improve retrieval accuracy. A test with a uniform wind field is used to demonstrate these conclusions.

Owing to a series of air pollution prevention and control policies, China’s PM_2.5 pollution has greatly improved; however, the long-term spatial contiguous products that facilitate the analysis of the distribution and variation of PM_2.5 pollution are insufficient. Due to the limitations of missing values in aerosol optical depth (AOD) products, the reconstruction of full-coverage PM_2.5 concentration remains challenging. In this study, we present a two-stage daily adaptive modeling framework, based on machine learning, to solve this problem. We built the annual models in the first stage, then daily models were constructed in the second stage based on the output of the annual models, which incorporated the parameter and feature adaptive tuning strategy. Within this study, PM_2.5 concentrations were adaptively modeled and reconstructed daily based on the multi-angle implementation of atmospheric correction (MAIAC) AOD products and other ancillary data, such as meteorological factors, population, and elevation. Our model validation showed excellent performance with an overall R² = 0.91 and RMSE = 9.91 μg/m³ for the daily models, along with the site-based cross-validation R²s and RMSEs of 0.86–0.87 and 12–12.33 μg/m³; these results indicated the reliability and feasibility of the proposed approach. The daily full-coverage PM_2.5 concentrations at 1 km resolution across China during the Three-Year Blue-Sky Action Plan were reconstructed in this study. We analyzed the distribution and variations of reconstructed PM_2.5 at three different time scales. Overall, national PM_2.5 pollution has significantly improved with the annual average concentration dropping from 33.67–28.03 μg/m³, which demonstrated that air pollution control policies are effective and beneficial. However, some areas still have severe PM_2.5 pollution problems that cannot be ignored. In conclusion, the approach proposed in this study can accurately present daily full-coverage PM_2.5 concentrations and the research outcomes could provide a reference for subsequent air pollution prevention and control decision-making.