This study empirically analyzes factors influencing the coupling and coordinated development of rural urbanization and rural finance. For this purpose, the study employs the coupling degree model and develops a panel quantile model to estimate the coupling degree and coupling coordination degree of rural urbanization and rural finance. Accordingly, the study presents panel data comprising 31 provinces, municipalities, and autonomous regions in China from 2010 to 2019. The empirical results reveal that the coupling degree of rural urbanization and rural finance is relatively low in most study areas. This result suggests that rural urbanization and rural financial development in most provinces in China have not exhibited coordinated development. Further, the results reveal that urban population density negatively affects the coupling and coordination degree of rural urbanization and rural finance. Moreover, the effects of urban population density are more significant in areas with a low coupling coordination degree compared with those with a higher coupling coordination degree. An increase in the quantile gradually decreases the effect of the proportion of educational expenditure to GDP on the degree of rural urbanization and rural financial coupling coordination. However, the effect of financial development efficiency increases accordingly. The per capita GDP, per capita fiscal expenditure, and per capita built-up area significantly affect all the quantiles, indicating a positive correlation. This correlation can promote the coupling and coordinated development of rural urbanization and rural finance. In areas with a low coupling coordination degree, the financial development scale significantly positively affects the coupling coordination degree of rural urbanization and rural finance. In areas with a high coupling coordination degree, the financial development scale significantly negatively correlates with the coupling coordination degree. In addition, a positive correlation exists between the financial development structure and the coupling coordination degree of rural urbanization and rural finance.

Accurate identification of key areas for ecological restoration is an important foundation for orderly promotion of ecological restoration of territorial space. This study identified the key areas for ecological restoration in Kangbao County according to the research paradigm of “source identification – resistance surface construction – corridor extraction”. First, ecological source sites were identified and screened based on habitat quality modeling, morphological spatial pattern analysis and landscape connectivity analysis. Then, the base resistance surface was constructed using ArcGIS software and nighttime light data. Thereafter, ecological corridors were extracted and ecological pinch points and ecological barrier points identified with the Linkage Mapper plug-in. Forty ecological source sites with a total area of 68.06 km² were identified in Kangbao County. These sites were dominated by woodland and grassland, and the integrity of these ecological source sites still needed to be improved. The 96 ecological corridors (total length of 743.81 km) were identified, which were densely distributed in the south and east parts of the study area, and there is still much room for improvement of the habitat connectivity. Additionally, 75 ecological pinch points (total area of 31.72 km²) and 69 ecological obstacles (total area of 16.42 km²) were identified. The key areas to be restored were mainly distributed in Yan Yufang, Har Chimega, Tuchengzi, Zhangji, and Danchenghe Townships. This study can lay a firm foundation for the ecological restoration in Kangbao County.

Knowledge about the dynamics and biophysical controlling mechanism of nocturnal evapotranspiration (ET_N) in desert-dwelling shrub ecosystem is still lacking. Using the eddy covariance measurements of latent heat flux in a dried shrubland in northwest China, we examined the dynamics of ET_N and its biophysical controls at multiple timescales during growing-seasons from 2012 to 2014. The ET_N was larger in the mid-growing season (usually in mid-summer) than in spring and autumn. The maximum daily ET_N was 0.21, 0.17, and 0.14 mm night⁻¹ in years 2012–2014, respectively. At the diel scale, ET_N decreased from 21:00 to 5:00, then began to increase. ET_N were mainly controlled by soil volumetric water content at 30 cm depth (VWC₃₀), by vapor pressure deficit (VPD) and normalized difference vegetation index (NDVI) at leaf expanding and expanded stage, and by air temperature (T_a) and wind speed (W_s) at the leaf coloring stage. At the seasonal scale, variations of ET_N were mainly driven by T_a, VPD, and VWC₁₀. Averaged annual ET_N was 4% of daytime ET. The summer drought in 2013 and the spring drought in 2014 caused the decline of daily evapotranspiration (ET). The present results demonstrated that ET_N is a significant part of the water cycle and needs to be seriously considered in ET and related studies. The findings here can help with the sustainable management of water in desert ecosystems undergoing climate change.

The relationship between land surface temperature (LST) and environmental factors is complex and nonlinear. To determine these relationships for China, this study analyzed the driving effects of air temperature, vegetation index, soil moisture, net surface radiation, precipitation, aerosols, evapotranspiration, and water vapor on LST based on remote-sensing and reanalysis data from 2003–2018, using a convergent cross-mapping method. During the study period, air temperature and net surface radiation were the dominant drivers of LST with a cross-mapping skill above 0.9. Vegetation index and evapotranspiration were the secondary drivers of LST with a cross-mapping skill that was higher than 0.5. Except for air temperature and net surface radiation, the direction and strength of the effects of the driving factors on LST were related to the climate type. The effects of air temperature and net radiation on LST diminished from north to south, indicating that LST was more sensitive to air temperature and net radiation in energy-limited regions. However, the effects of vegetation index and evapotranspiration on LST varied significantly across climate zones; that is, positive effects were mostly in non-monsoonal zones and negative effects were primarily in monsoonal zones. Our results quantified the driving role of environmental factors on LST and provided a comprehensive understanding of LST dynamics.

Escalating urban issues in Beijing call for comprehensive exploration of urban construction land expansion towards the goal of carbon neutrality. Firstly, urban construction land in Beijing during the period 2005–2020 was accurately detected using Landsat images and impervious surface data, and then its expansion characteristics were revealed. Finally, the driving mechanism of urban construction land expansion was explored using geographically and temporally weighted regression from the input–output perspective. The results showed that the expansion speed and intensity of urban construction land in Beijing showed an overall tendency to slow down, and the center of urban expansion shifted to the new urban development zone and ecological function conservation zone. Urban construction land expansion in the central urban area was first scattered and then compact, while that in the new urban development zone and ecological function conservation zone primarily followed an outward pattern. The permanent population, per capita GDP, and per capita retail sales of social consumer goods were the primary driving factors of urban construction land expansion in Beijing, the impacts of which varied significantly among different districts of Beijing. All these results can provide a solid foundation for improving land use policies towards the goal of carbon neutrality in highly urbanized areas.

Relative humidity (RH) is one of the key variables for understanding the water, energy, and carbon exchange between the Earth and the atmosphere. Traditional methods for deriving RH from remotely sensed data usually require ground meteorological observations or are limited to clear-sky conditions, thereby making it a significant challenge to obtain spatially complete RH under all-sky conditions, especially over the regions with sparse meteorological instruments for observation. To this end, a new approach for deriving all-sky RH entirely based on Moderate Resolution Imaging Spectroradiometer (MODIS) data was proposed in the present study. Two key assumptions in the approach under cloudy conditions are that the actual water vapor is linearly related to the total precipitable water vapor (PWV) and that air temperature is linearly related to land surface temperature (LST). Results from a total of 30 AmeriFlux stations proved the aforementioned assumptions based on MODIS data collected over a study period of three years from 2009 to 2011. For different aridity conditions, RH retrieval revealed reasonable accuracy with a root-mean-square error (RMSE) of approximately 15.3% over an arid and semiarid region, whereas a comparable RMSE of 17.0% was obtained over a humid area. Further results also indicated that the aforementioned linear relationships were generally temporally stable, thereby indicating that the proposed method can be used to obtain all-sky RH at a regional or global scale entirely based on MOD06_L2-derived LST and MOD05_L2-derived PWV data given that the assumed linear relationships can be easily determined by historical MOD07_L2-derived atmospheric profiles.