Shidong Liu

Soil organic carbon (SOC) is a major terrestrial carbon pool, and its maintenance is important for climate change mitigation. Straw retention is widely used in croplands, but the association between crop residue cover (CRC) and soil organic carbon density (SOCD) may be nonlinear and may vary under peri-urban thermal conditions. We integrated multi-source spatial datasets for Mollisol croplands in Northeast China to compare recent mean SOCD in the upper 20 cm between stable rural and rural-to-urban transition zones and to examine the associations among CRC, urban heat island (UHI) intensity, and SOCD. XGBoost combined with SHapley Additive exPlanations (SHAP) was used to quantify predictor importance and characterize the nonlinear contribution of CRC to predicted SOCD. Structural equation modeling and partial least squares path modeling were further used to assess statistical associations and interaction terms among CRC, daytime and nighttime UHIs, and SOCD. Mean SOCD was slightly higher in the transition zone than in the stable zone, whereas mean CRC did not differ significantly between the two zones. The contribution of CRC to predicted SOCD showed nonlinear dependence patterns in both zones, with a similar turning region at approximately 48–50% CRC. The lower-CRC patterns differed between zones, whereas CRC was strongly positively correlated with its SHAP value across the higher-CRC interval in both zones. Daytime and nighttime UHIs were associated with contrasting CRC-SHAP patterns, and the estimated interaction effects differed between the stable and transition zones. These results reveal nonlinear and spatially heterogeneous associations between CRC and SOCD under peri-urban thermal conditions and provide a basis for further temporal and process-based investigation.

Dense humid forests in Central Africa hold vast biomass carbon stocks but are increasingly affected by small-scale disturbances that are difficult to detect with current satellite observations. Here, we generate 10 m canopy height maps for 2019–2022 using deep learning with spaceborne lidar and Sentinel-1/−2 imagery, and derive aboveground biomass changes at 30 m resolution. The maps reveal widespread small disturbance patches, 87% smaller than 1 ha. These account for 48% of biomass carbon gains in regrowing forests and 37% of carbon losses from deforestation. We estimate a net forest carbon loss of −58 ± 14 Tg C yr⁻¹, with gross losses of −126 ± 21 Tg C yr⁻¹ partially offset by gains of 68 ± 10 Tg C yr⁻¹. The Democratic Republic of the Congo emerges as a net carbon source (−46 ± 11 Tg C yr⁻¹), representing 79% of the regional loss, primarily due to degradation, despite having the largest carbon gains in young secondary forests. Our remote-sensing estimates of national carbon budgets are broadly consistent with country-level inventories and bookkeeping model estimates, paving the way for spatially explicit and transparent carbon monitoring.

Shifting cultivation (SC) is a predominant land use across the tropics, feeding hundreds of millions of marginalized people, causing significant deforestation in tropical regions. A key question is how to realize rapid and large-scale identification of the spatial distribution, cycle numbers, and fallow periods of SC. Building the LandCycler algorithm that fully considers the inter-annual cycle of SC based on Landsat imagery from 1988 to 2020, we identify the distribution and fallow period of SC in Southeast Asia, including Assam in India and Yunnan Province in China. The results show that the LandCycler for the identification of SC is satisfactory (producer’s accuracy 82.12% and user’s accuracy 81.37%), and the accuracy in detecting the average cycle number, and calculating the average fallow period is 83.71%, and 96%, respectively. We found that the total area of SC is as high as 16.79 × 10⁴ km² in Southeast Asia, which uses almost 10% of the total forests. Meanwhile, the average cycle number and the average fallow period of SC are two times and 10 years, respectively. More than 98% of SC has repeated deforestation four times or less. The shorter the distance from settlements and the distance from roads, the larger the cycle number of SC. Although there was no significant correlation between elevation and slope and the cycle number of SC, SCs were mainly distributed at slopes of 18 ± 5° and elevations of 800 ± 300 m. These findings provide effective tools for sustainable agroforestry management as well as for global SC mapping.

The spatiotemporal dynamics of the Air Quality Index (AQI) and its response to vegetation regulation require further investigation. Using multi-source data from 2020 in Shandong Province, China, this study analyzed the effects of vegetation greenness (NDVI, LAI, EVI), ecological efficiency (Net Primary Productivity, NPP), and landscape structure on AQI within 3 km grids. Monthly correlation analyses revealed that AQI peaks in January (125.09), June (99.01), and December (105.81). PM_2.5, O₃, and PM₁₀ were the primary pollutants in winter, summer, and spring/autumn, respectively. Vegetation showed a significant purifying effect from June to September. NPP (r = −0.83) was more effective in mitigating air pollution than greenness-related indices (r = −0.48). Pollution mitigation was enhanced by vegetation patches with complex shapes and dispersed configurations. During the non-growing season, the vegetation alleviating effect weakened considerably, and a decoupling between greenness and ecological efficiency occurred. This decoupling was associated with a stronger positive correlation between population density and AQI. The findings highlight the importance of seasonal vegetation dynamics and landscape optimization for regional air quality management.

Dense humid forests in Central Africa hold vast biomass carbon stocks but face increasing pressure from logging, clearings, and fire disturbances. Many of these disturbance events have a small spatial scale and are missed by current satellite observations. To address this knowledge gap we generated annual 10 meter resolution maps of canopy height from 2019 to 2022 using a deep learning model fusing spaceborne lidar height measurements with Sentinel radar and optical imagery. The maps reveal widespread previously undetected small disturbance patches, with 87% of all the disturbances being of less than 1 ha in size. These disturbance patches smaller than 1 ha account for 48% of the biomass carbon gains in regrowing forests and for 37% of carbon losses from deforestation. We found a net carbon loss of −58 ± 9 Tg C yr−1 composed of a gross loss of −126 ± 7 Tg C yr−1 partially offset by a gain of 68 ± 6 Tg C yr−1, implying a turnover of biomass carbon from disturbances of 0.52% per year. The Democratic Republic of the Congo is a small net source of carbon (−46 ± 6 Tg C yr−1) due to degradation, despite having the largest carbon gains in young secondary forests. Across the Congo Basin, protected areas show a net biomass loss of −4 ± 1 Tg C yr−1, with a gross loss of −14 ± 1 Tg C yr−1, highlighting uneven conservation outcomes. Our remote-sensing data aggregated into national carbon budgets align well with country-level inventories and bookkeeping model estimates, paving the way for spatially explicit and transparent carbon monitoring.

Forests play a pivotal role in global carbon cycling and biodiversity conservation, yet they face increasing disturbances from both anthropogenic and natural drivers. This study presents the first high-resolution (30 m) global forest disturbance dataset (GFD) for 2000–2020, classifying 11 disturbance types by integrating Landsat-based Continuous Change Detection and Classification (CCDC) time-series analysis with spatial metrics and machine learning. A total of 57 000 expert-validated sample points were used to train and validate a decision tree model, achieving an overall accuracy of 99 %. The results reveal that forestry replanting (44 %), shifting cultivation (24 %), and forest fires (11 %) dominate global forest loss. There are regional differences in global forest disturbance, such as farmland expansion in South America and Africa, forest fires in northern regions, and shifting cultivation in tropical regions. Disturbed forests span 1247.06 Mha, accounting for 31 % of the global forest area. Notably, 3 % of global forests were newly established, primarily in China, India, and Brazil. The spatial consistency analysis (R²= 0.93) highlights a strong overall agreement between the GFD product and other datasets, while the GFD product offers superior spatial resolution. The GFD dataset advances our understanding of forest dynamics and underscores the need for targeted conservation strategies in an era of escalating environmental change. The 30 m resolution GFD generated by this study is openly available at https://doi.org/10.6084/m9.figshare.28465178 (Liu et al., 2025a).

The conversion of Chinese farmland to non‐main grain cropland (NMGL) threatens national food security, yet its local‐scale dynamics remain poorly understood. Here, we provide a county‐level analysis of the spatiotemporal patterns and drivers of this land‐use change. We identify striking regional disparities: the prevalence of NMGL is highest in major grain consumption areas (22.3%), intermediate in balanced zones (18.4%), and lowest in production areas (11.6%). Between 1985 and 2020, this trend evolved from localized occurrences into a widespread phenomenon. Crop systems influence conversion risks, with single‐cropping systems exhibiting a 2.1–3.8 times higher vulnerability to conversion than intercropped systems. Driver analysis indicates that soil factors (particularly organic carbon) are the primary determinant nationwide (40% contribution), although socioeconomic factors prevail in balanced regions. To effectively preserve China's agricultural land, management frameworks need transition from rigid regulation of non‑grain cropping areas toward a dynamic protection strategy anchored in food‑balance demands. A smarter, system‐wide approach is needed that optimizes planting structures and agricultural layouts. Our study provides critical insights for formulating a food balance demand‐oriented farmland protection strategy.

Rapid urbanization in China has exacerbated the dual challenges of urban heat islands (UHIs) and air pollution, threatening urban sustainability. We conducted a national-scale analysis of the spatiotemporal dynamics and synergy between the surface UHI intensity, distinguished as daytime (DUHI) and nighttime (NUHI), and major air pollutants (PM_2.5, PM₁₀, NO₂) in 370 Chinese cities (2000–2019). Using multi-source remote sensing, ground-based monitoring, and urban data, we applied coupling coordination and correlation analyses to quantify these interactions. Key findings reveal distinct patterns: (1) The annual mean land surface temperature (LST) rose, with the nighttime LST (NLST) increasing faster than the daytime LST (DLST). Conversely, the UHI intensity showed an overall decline, with the DUHI decreasing more than the NUHI. (2) Air pollutants displayed strong seasonality; while PM10 concentrations decreased slightly over the long term, NO₂ levels rose significantly. (3) Monthly, pollutants correlated negatively with LST (R² > 0.92 for PM_2.5), suppressing the DUHI but intensifying the NUHI. Long-term, the correlation trend revealed a strengthening synergy, particularly between particulate matter and NUHI (trend R² = 0.50). (4) Spatially, over 90% of cities exhibited high UHI–particle coordination. Key associated factors include anthropogenic activities, urban morphology, and natural mitigation factors. We conclude that disrupting the heat–pollution synergy requires integrated strategies, namely reducing emissions at the source, optimizing the urban form, and enhancing ecological regulation. This is essential for advancing low-carbon, climate-resilient urban development.

Detecting tree cover is crucial for sustainable land management and climate mitigation. Here we develop an automatic detection algorithm using high-resolution satellite data (<5 m) to map pan-tropical tree cover (2015–2022), enabling identification and change analysis for previously undetected tree cover (PUTC). Our findings reveal that neglecting PUTC represents 17.31 ± 1.78% of the total pan-tropical tree cover. Tree cover net decreased by 61.05 ± 2.36 Mha in both forested areas (63.93%) and non-forested areas (36.07%) between 2015 and 2022. Intense changes in tree cover are primarily observed in regions with PUTC, where the World Cover dataset with a resolution of 10 m often fails to accurately detect tree cover. We also conduct a sensitivity analysis to quantify the contributions f climate factors and anthropogenic impacts (including human activities and land use cover change) to tree cover dynamics. Our findings indicate that 43.98% of tree cover gain is linked to increased precipitation, while 56.03% of tree cover loss is associated with anthropogenic impacts. These findings highlight the need to include undetected tree cover in strategies combating degradation, climate change, and promoting sustainability. Fine-scale mapping can improve biogeochemical cycles modeling and vegetation-climate interactions, improving global change understanding.