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.

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.

Agricultural drought threatens China's food and ecological security, and accurate spatio-temporal monitoring is key for disaster mitigation. Soil moisture is critical for drought assessment, but the accuracy of existing remote sensing-based SM products in China remains to be improved. This study develops a framework that synergistically integrates a spatiotemporally adaptive gap-filling algorithm with a machine learning-based downscaling approach, generating a seamless 0.05° monthly SM dataset for China from 2003 to 2023. The methodology harnesses the complementary strengths of random forest modeling and spatiotemporal reconstruction techniques to effectively fuse multi-source satellite observations, achieving dual improvements in SM data accuracy and spatial coverage. Using this dataset, the standardized soil moisture index was applied to characterize the spatio-temporal evolution of agricultural drought. Results demonstrate that (1) The downscaled SM dataset achieves significant improvements in both spatial resolution and accuracy, showing a 2.3–34.4 % reduction in ubRMSE and 1.2–52.7 % improvement in correlation coefficients compared to benchmark datasets. (2) Drought characterization based on the downscaled SM dataset and SSI accurately identified the extent of agricultural drought, showing a significant spatiotemporal consistency with agricultural disaster area. (3) Agricultural drought intensified significantly across China during the study period, characterized by northward migration of drought center and spatially heterogeneous aridification patterns – decreasing severity from northwest to southeast while increasing from northeast to southwest. High-frequency drought zones were predominantly clustered in ecologically vulnerable regions, particularly the agro-pastoral ecotone of northern China. (4) Distinct intra-annual drought dynamics emerged, with a southwest-to-northeast expansion dominating from January to June, followed by bidirectional propagation from the Yellow River-Huaihe River Basin (YRB-HRB) to northwestern and southeastern regions from June to December. This study provides high-accuracy data support for agricultural drought monitoring and offers scientific insights for developing regional differentiated drought mitigation strategies, which are of great significance for ensuring national food security.

Agricultural drought threatens China's food and ecological security, and accurate spatio-temporal monitoring is key for disaster mitigation. Soil moisture is critical for drought assessment, but the accuracy of existing remote sensing-based SM products in China remains to be improved. This study develops a framework that synergistically integrates a spatiotemporally adaptive gap-filling algorithm with a machine learning-based downscaling approach, generating a seamless 0.05° monthly SM dataset for China from 2003 to 2023. The methodology harnesses the complementary strengths of random forest modeling and spatiotemporal reconstruction techniques to effectively fuse multi-source satellite observations, achieving dual improvements in SM data accuracy and spatial coverage. Using this dataset, the standardized soil moisture index was applied to characterize the spatio-temporal evolution of agricultural drought. Results demonstrate that (1) The downscaled SM dataset achieves significant improvements in both spatial resolution and accuracy, showing a 2.3–34.4 % reduction in ubRMSE and 1.2–52.7 % improvement in correlation coefficients compared to benchmark datasets. (2) Drought characterization based on the downscaled SM dataset and SSI accurately identified the extent of agricultural drought, showing a significant spatiotemporal consistency with agricultural disaster area. (3) Agricultural drought intensified significantly across China during the study period, characterized by northward migration of drought center and spatially heterogeneous aridification patterns – decreasing severity from northwest to southeast while increasing from northeast to southwest. High-frequency drought zones were predominantly clustered in ecologically vulnerable regions, particularly the agro-pastoral ecotone of northern China. (4) Distinct intra-annual drought dynamics emerged, with a southwest-to-northeast expansion dominating from January to June, followed by bidirectional propagation from the Yellow River-Huaihe River Basin (YRB-HRB) to northwestern and southeastern regions from June to December. This study provides high-accuracy data support for agricultural drought monitoring and offers scientific insights for developing regional differentiated drought mitigation strategies, which are of great significance for ensuring national food security.

Agricultural drought threatens China's food and ecological security, and accurate spatio-temporal monitoring is key for disaster mitigation. Soil moisture is critical for drought assessment, but the accuracy of existing remote sensing-based SM products in China remains to be improved. This study develops a framework that synergistically integrates a spatiotemporally adaptive gap-filling algorithm with a machine learning-based downscaling approach, generating a seamless 0.05° monthly SM dataset for China from 2003 to 2023. The methodology harnesses the complementary strengths of random forest modeling and spatiotemporal reconstruction techniques to effectively fuse multi-source satellite observations, achieving dual improvements in SM data accuracy and spatial coverage. Using this dataset, the standardized soil moisture index was applied to characterize the spatio-temporal evolution of agricultural drought. Results demonstrate that (1) The downscaled SM dataset achieves significant improvements in both spatial resolution and accuracy, showing a 2.3–34.4 % reduction in ubRMSE and 1.2–52.7 % improvement in correlation coefficients compared to benchmark datasets. (2) Drought characterization based on the downscaled SM dataset and SSI accurately identified the extent of agricultural drought, showing a significant spatiotemporal consistency with agricultural disaster area. (3) Agricultural drought intensified significantly across China during the study period, characterized by northward migration of drought center and spatially heterogeneous aridification patterns – decreasing severity from northwest to southeast while increasing from northeast to southwest. High-frequency drought zones were predominantly clustered in ecologically vulnerable regions, particularly the agro-pastoral ecotone of northern China. (4) Distinct intra-annual drought dynamics emerged, with a southwest-to-northeast expansion dominating from January to June, followed by bidirectional propagation from the Yellow River-Huaihe River Basin (YRB-HRB) to northwestern and southeastern regions from June to December. This study provides high-accuracy data support for agricultural drought monitoring and offers scientific insights for developing regional differentiated drought mitigation strategies, which are of great significance for ensuring national food security.

Agricultural drought threatens China's food and ecological security, and accurate spatio-temporal monitoring is key for disaster mitigation. Soil moisture is critical for drought assessment, but the accuracy of existing remote sensing-based SM products in China remains to be improved. This study develops a framework that synergistically integrates a spatiotemporally adaptive gap-filling algorithm with a machine learning-based downscaling approach, generating a seamless 0.05° monthly SM dataset for China from 2003 to 2023. The methodology harnesses the complementary strengths of random forest modeling and spatiotemporal reconstruction techniques to effectively fuse multi-source satellite observations, achieving dual improvements in SM data accuracy and spatial coverage. Using this dataset, the standardized soil moisture index was applied to characterize the spatio-temporal evolution of agricultural drought. Results demonstrate that (1) The downscaled SM dataset achieves significant improvements in both spatial resolution and accuracy, showing a 2.3–34.4 % reduction in ubRMSE and 1.2–52.7 % improvement in correlation coefficients compared to benchmark datasets. (2) Drought characterization based on the downscaled SM dataset and SSI accurately identified the extent of agricultural drought, showing a significant spatiotemporal consistency with agricultural disaster area. (3) Agricultural drought intensified significantly across China during the study period, characterized by northward migration of drought center and spatially heterogeneous aridification patterns – decreasing severity from northwest to southeast while increasing from northeast to southwest. High-frequency drought zones were predominantly clustered in ecologically vulnerable regions, particularly the agro-pastoral ecotone of northern China. (4) Distinct intra-annual drought dynamics emerged, with a southwest-to-northeast expansion dominating from January to June, followed by bidirectional propagation from the Yellow River-Huaihe River Basin (YRB-HRB) to northwestern and southeastern regions from June to December. This study provides high-accuracy data support for agricultural drought monitoring and offers scientific insights for developing regional differentiated drought mitigation strategies, which are of great significance for ensuring national food security.