The impact of human activities on vegetation has been the focus of much research, but the impact on radiation energy through surface albedo associated with vegetation greenness and length of the growth season is still not well documented. Based on the land cover data for the years 2000 and 2015, this study first divided the land cover change in Beijing from 2000 to 2015 into five types according to the impact of human activities and vegetation resilience, namely, old urban areas (OU), urban expansion areas (UE), cropland (CP), mixed pixel areas (MP, which means the land covers other than urban expansion which had changed from 2000 to 2015), and the residual vegetation cover areas (pure pixels (PP), dominated by natural and seminatural vegetation, such as grassland, forest, and wetland). Then, we calculated the direct radiative forcing from the albedo change from 2000 to 2015 and analyzed the effect of vegetation on the albedo under different land cover types based on multi-resource Moderate Resolution Imaging Spectroradiometer (MODIS) products of vegetation, albedo, and solar radiation. The results showed that the most typical changes in land cover were from urban expansion. By comparing the PP with the four human-affected land cover types (OU, UE, MP, and CP), we confirmed that the radiative forcing increment between 2001–2003 and 2013–2015 in PP (0.01 W/m²) was much smaller than that in the four human-affected land cover types (the mean increment was 0.92 W/m²). This study highlights that human activities affected vegetation growth. This, in turn, brought changes in the albedo, thereby enhancing radiative forcing in Beijing during 2000–2015.

Current cities are not concrete jungles and deserts with sparse vegetation. Urban green space (UGS) appears widely in human activity areas and plays an important role in improving the human living environment and accumulates carbon storage. However, given the scattered distribution of UGS, studies on both the refined spatial estimation of carbon storage and appropriate mapping scale are still lacking. Taking the downtown area of Kaifeng, China, as the study area, this study verified the i-Tree Eco model on the basis of a field survey and accurately estimated the spatial carbon storage of UGS by combining it with remote sensing data, and finally, we obtained the minimum spatial mapping scale of UGS carbon storage by scaling. The results showed that (1) the total area of UGS in study area was 26.41 km², of which the proportion of total area of residential area and park green spaces was about 50%. The area of UGS per capita in the study area is 40.49 m². (2) Within the 123 survey samples, the proportion of communities with tree–shrub–herbs structure was the highest, 51.22%. The average carbon density was 5.89 kg m⁻², among which the park, protective and square green spaces had the highest carbon density in all land use types. (3) The total carbon storage of UGS in the study area was 114,389.17 t, and the carbon storage of UGS per capita was 175.39 kg. Furthermore, the scaling analysis showed that 0.25 km spatial resolution was the minimum spatial scale for UGS carbon storage mapping. This study improves our understanding of urban carbon storage, highlights the role and potential of UGS in carbon neutrality, and clarifies the importance of estimating urban carbon storage at appropriate scales. This study is also of great significance for rationally understanding the terrestrial carbon cycle in urban areas and improving regional climate simulations.

The greenhouse gases sequestrated by ecosystems are of great relevance to global carbon cycle and climate regulation. However, it is time-consuming and laborious to conduct sampling analysis, and it is also difficult to analyze the variation of potential sequestration of various ecosystems for greenhouse gases in China. This study used six 5-year periods of land use data for China between 1990 and 2015 to analyze the changes of three natural ecosystems (forest, grassland, and wetland). Correspondingly, the potential sequestration of the three ecosystems for three major greenhouse gases (carbon dioxide CO₂, methane, and nitrous oxide) during the 25 years were simulated through a greenhouse gas value (GHGV) model. The GHGV model was found to be a reliable alternative to calculating the carbon sequestration of natural ecosystems in China. The total greenhouse gas sequestration of China’s natural ecosystems remained at around 267 Pg CO₂-equivalent; however, the greenhouse gas sequestration had decreased by 3.3 Pg CO₂-equivalent between 1990 and 2015. Comparison of the simulation results of the GHGV model based on the localized parameters and the model default parameters revealed that the simulated potential sequestration of the greenhouse gases for forest and wetland ecosystems (but not the grassland ecosystem) were smaller when run with localized parameters than the model default parameters. Moreover, the carbon sequestration of natural ecosystems was greater than the amount of anthropogenic carbon emissions, but the potential sequestration of natural ecosystems for greenhouse gas has become increasingly limited. Our study reveals the model can act as an important supplement for assessing the potential sequestration of the greenhouse gases for ecosystems at a regional scale.

Land cover change (LCC) and its impact on CO₂ sequestration and radiative forcing (RF) could dramatically affect climate change, but there has been little effort to address this issue in South and Southeast Asia over a long period of time using actual land cover information. In this study, annual land cover data from 1992 to 2015 were used to assess the CO₂ flux and corresponding RF due to LCC in South and Southeast Asia. The results showed that 553.2 × 10³ km² of the region experienced LCC during this period, mostly due to land reclamation, urban expansion, and deforestation. These LCC caused a marked net decrease in net ecosystem productivity (NEP) as a composite of the various land cover categories during the whole study period, especially since 2001. The CO₂ sequestration was 2160 TgCO₂ during the early 1990s however cumulative sequestration decreased by 414.95 TgCO₂ by 2015. Correspondingly, the cooling effect of NEP, i.e. the total actual RF, was −0.366 W m⁻² in South and Southeast Asia between 1992 and 2015. However, the potential RF of the cumulatively reduced NEP due to LCC relative to the 1990s resulted in a warming effect of 2.33 × 10⁻³ W m⁻² in 2015. Our study provides an applicable framework to accurately assess the potential effect of large-scale LCC on climate.

Both cropland and climate change over time, but the potential effects of climate change on cropland is currently not well understood. Here, we combined temporally and spatially explicit dynamics of cropland with air temperature, precipitation, and solar radiation datasets. China’s cropland showed a clear northward-shifting trend from 1990 to 2015. The cropland decreased south of the break line at 38° N, whereas it increased from the break line to northern regions. Correspondingly, the temperature showed a significant warming trend in the early part of the study period, which slowed down in later years. During the whole study period, both precipitation and solar radiation decreased over time, showed no significant linear characteristics, and the annual fluctuations were very large. The cropland areas in China showed a displacement characteristic with the increasing temperature, precipitation, and radiation. Overall, the cropland was shifting towards the high-temperature, low-precipitation, and low-radiation areas. The cropland dynamics indicate that they are likely to face severe drought and radiation pressure. Our findings imply that more resources such as irrigation may be needed for cropland, which will undoubtedly aggravate the agricultural water use in most northern regions, and the potential impacts on food security will further emerge in the future.

The principal difficulty in extracting urban surface water using remote-sensing techniques is the influence of noise from complex urban environments. Although various methods exist, there are still many sources of noise interference when extracting urban surface water, and automatic cartographic methods with long time series are especially scarce. Here, we construct an automatic urban surface water extraction method from the combination of traditional water index, urban shadow index (USI), and land surface temperature (LST) by using the Google Earth Engine cloud computing platform and Landsat imagery. The three principal findings derived from the application of the method were as follows. (i) In comparison with autumn and winter, LST in spring and summer could better distinguish water from high-reflection ground objects, shadows, and roads and roofs covered by asphalt. (ii) The overall accuracy of Automated Water Extraction Index (AWEIsh) in Zhengzhou was 77.5% and the Kappa coefficient was 0.55; with consideration of the USI and LST, the overall accuracy increased to 96.0% and the Kappa coefficient increased to 0.92. (iii) During 1990–2020, the area of urban surface water in Zhengzhou increased, with an evident trend in expansion from 11.51 km² in 2008 to 49.28 km² in 2020. Additionally, possible omissions attributable to using 30m-resolution imagery to extract urban water areas were also discussed. The method proposed in this study was proven effective in eliminating the influence of noise in urban areas, and it could be used as a general method for high-accuracy long-term mapping of urban surface water.