Image analysis of the Yellow River beach area since 1987 provided land use and water body patterns to support effective agricultural and environmental management. Landsat and Sentinel-2A/B images, and data from the Third National Land Survey, were used to examine the water body and land use patterns. The continuous beach land since 1987 was calculated from annual vegetation and water body indexes while that of cultivated land was extracted from the Third National Land Survey. Object-Oriented Feature Extraction was used to extract staple crops. The results showed that 58.26% of the beach area was cultivated land. Continuous beach land covered an area of 1630.98 km² and was consisted of scattered patches that were unevenly distributed between the north and south banks of the Yellow River. The staple crop types in the beach area, winter wheat and summer corn accounted for 72.37% and 68.03% of the total cultivated land. Affected by the strategy on the Yellow River basin in China, as the ecological space and protection continue to increase, this study provides basic scientific references for the correct use of cultivated land resources and protection of the balance of soil and water resources dynamic utilization and balance of cultivated land protection and ecological protection.

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.

The South-to-North Water Diversion (SNWD) provides significant benefits in facilitating water security and improving ecology in northern China. However, few studies have estimated the water value of the SNWD and the corresponding subsequent subsidies of the ecological migrants in Xichuan County displaced by the project. Based on the Google Earth Engine (GEE), this study analyzed the water ecosystem changes in Xichuan County in 2000–2020 and valued the water transfer of the SNWD. We calculated the water cost, the water value of the trunk line project, and the four provinces (Hebei, Henan, Beijing, and Tianjin) of CNY 4.04, 39.64, and 120.93 billion, respectively, and the proportion of the three was 1:10:30 during 2014–2020. The water ecosystem area showed a rapid increase when the SNWD became operational since the end of 2014. The subsequent annual subsidy gap of ecological migrants was CNY 0.84 billion, which only accounted for 4.31% of the gross profit of SNWD. Our results imply that relevant water sectors have sufficient profits to support corresponding subsequent subsidies for ecological migrants. Ecological migrants are a major challenge for water transfer projects. Overall, this study fills a gap of interactions between subsequent policies and ecological migrants and provides a typical case for managing the migration problem caused by sustainable water management worldwide.

CO₂ undisputedly affects global temperature change, but the specific impact of change in atmospheric CO₂ concentration on regional warming remains to be quantified, especially in different climatic backgrounds. Taking the Loess Plateau as the research area, this study quantified the effect of CO₂ elevation on regional temperature change based on a single-factor sensitivity experiment of the regional Weather Research and Forecasting (WRF) climatic model, and the results revealed the following: (i) The correlation coefficient between monthly mean values of temperature simulated by the WRF model and the observed values reached 0.96 (p < 0.01), and the overall spatial trends of simulated and observed temperatures increased from the northwest to the southeast. (ii) CO₂ concentration increased from 370.70 ppm in 2000 to 414.54 ppm in 2020, and the Loess Plateau region warmed by 0.04 and 0.06 °C under the MODIS land cover of 2000 and 2020, respectively. This indicates that increase in CO₂ concentration over the Loess Plateau has greater impact than land cover change on regional temperature change. (iii) As CO₂ concentration increased, the maximum fluctuation of temperature in summer exceeded 2.0 °C, while the fluctuations in spring (0.72 °C), autumn (0.77 °C), and winter (0.15 °C) were relatively small, indicating that summer temperature is most sensitive to CO₂ concentration change. By emphasizing the marked temperature difference associated with the same CO₂ change in different seasons, this study provides an important basis for extending the understanding of the differences in the effect of CO₂ on regional temperatures.

High-resolution above-ground biomass (AGB) data play a critical role in advancing low-carbon development strategies across cities. However, research on urban trees’ AGB largely relies on high-accuracy field measurements, which limits the feasibility of conducting cross-regional studies. In contrast, existing remote-sensing-based AGB products provide extensive coverage while lacking the spatial resolution required for precise city-scale analysis. To address the dilemma of achieving both high spatial resolution and broad coverage, this study integrated 149 feature variables derived from multi-source datasets and implemented quality-control procedures to select high-quality samples from two globally representative AGB products (GEDI AGB and CCI AGB). This strategy substantially improved the performance of the random forest model and generated 10 m resolution urban trees’ AGB maps for 51 C40 cities across Eurasia continent. The results indicate that: (1) after applying quality control to the target variables, the mean R² of ten-fold cross validation improved from 0.37 to 0.75, and the MAE decreased substantially from 47.02 Mg/ha to 17.48 Mg/ha; (2) by enhancing the spatial resolution of AGB maps to 10 m, the resulting products exhibit superior spatial detail, better capture local variations, and maintain greater spatial continuity compared with the CCI AGB and GEDI AGB datasets; (3) the mean AGB density across the Eurasian continent was 39.44 Mg/ha, with total urban tree s’ AGB reaching 83.83 × 10⁶ t. Comparison with previous single-city C40 studies shows that our estimated AGB density and total AGB closely align with previously reported values. The above data implies that cities carry an undeniable amount of carbon storage, both in terms of carbon density and total amount. This study provides a robust foundation for accurately assessing the potential of urban carbon sinks and optimizing the path to achieving carbon neutrality.