66 citations as recorded by crossref.

1. 2000–2023年粤港澳大湾区城市土地利用碳代谢时空演变与驱动机制 少. 王 et al.
2. OpenForest: a data catalog for machine learning in forest monitoring A. Ouaknine et al.
3. Principle, technique and application of grassland improvement X. Huang & F. Hou
4. The stand age governs forest root: Shoot ratios across northeast China H. Wu et al.
5. Pattern and driver of Rosa roxburghii Tratt suitability and its effect on ecological services in karst desertification control areas X. Min et al.
6. Dynamics of Aboveground Carbon Across Karst Terrestrial Ecosystems in China from 2015 to 2021 J. Shi et al.
7. Evaluation of optical and microwave-derived vegetation indices for monitoring aboveground biomass over China Z. Chang et al.
8. Estimating Aboveground Biomass of Boreal Forests in Northern China Using Multiple Datasets J. Li et al.
9. Quantifying the impact pathways and driving mechanisms of increased forest CS: A comparative study case from typical karst ecologically fragile and non-karst areas Z. Zhou et al.
10. Carbon sequestration potential of tree planting in China L. Yao et al.
11. Future response trend of forest carbon storage to land cover change in China under climate change X. Bai et al.
12. Development of a finer-resolution multi-year emission inventory for open biomass burning in Heilongjiang Province, China B. Li et al.
13. Small vegetation biomass retention efficiency across global forests J. Chen et al.
14. Integrating tree species composition with height-driven allometry for enhanced aboveground biomass mapping P. Liu et al.
15. Spatiotemporal Dynamics of Tree Species-Level Aboveground Carbon Storage at the Canal Scale Under Green Engineering with a Random Forest Model W. Wang et al.
16. A modeling framework for forest aboveground biomass estimation in mixed forests of the southeastern United States using airborne lidar and PlanetScope data N. Thapa et al.
17. Estimation of unrealized forest carbon potential in China using time-varying Boruta-SHAP-random forest model and climate vegetation productivity index T. Li et al.
18. Sustainable growth of China’s forest biomass carbon storage since 2002: Facing threats and loss risks Q. Lv et al.
19. IIDM: Improved implicit diffusion model with knowledge distillation to estimate the spatial distribution density of carbon stock in remote sensing imagery Z. Yu et al.
20. Climate Factors Affect Above–Belowground Biomass Allocation in Broad-Leaved and Coniferous Forests by Regulating Soil Nutrients X. Zhang et al.
21. Vapor pressure deficit dominates dryness stress on forest biomass carbon in China under global warming Y. Cen et al.
22. Unveiling the spatiotemporal dynamics and influencing factors of carbon stocks in the yangtze river basin over the past two decades H. Xi & T. Li
23. An integrated approach to assess spatial and temporal changes in the contribution of the ecosystem to sustainable development goals over 20 years in China Z. Ma et al.
24. Forest aboveground carbon storage estimation and uncertainty analysis by coupled multi-source remote sensing data in Liaoning Province H. Fu et al.
25. Optimal spatiotemporal thinning schemes combined with wood vaulting could maximize the forest biomass carbon sink in China Y. Chen et al.
26. Substantial contribution of trees outside forests to above-ground carbon across China Y. Su et al.
27. Spatio-Temporal Evolution and Influencing Factors of Ecological Resilience: A Human-Land Relationship Perspective H. Sa et al.
28. Integrating Ecosystem Services into Ecological Zoning Management: Insights from the Shan-Shui Initiative in China H. Xia et al.
29. Separating Biomass Gains and Losses of Planted Forest and Natural Forest and Their Contributions to Forest Biomass Carbon Storage in China for 2005–2020 H. Yan et al.
30. Factors driving carbon accumulation in forest biomass and soil organic carbon across natural forests and planted forests in China T. Wang et al.
31. Spatiotemporal analysis of AGB and BGB in China: Responses to climate change under SSP scenarios C. Zhu et al.
32. A Novel Forest EcoSpatial Network for Carbon Stocking Using Complex Network Theory in the Yellow River Basin H. Zhang et al.
33. China’s Climate Change Policy Attention and Forestry Carbon Sequestration Growth J. Meng et al.
34. Assessing the Trade-Offs and Synergies Among Ecosystem Services Under Multiple Land-Use Scenarios in the Beijing–Tianjin–Hebei Region X. He et al.
35. Spatiotemporal evolution and driving mechanisms of urban land use carbon metabolism in the Guangdong-Hong Kong-Macao Greater Bay Area from 2000 to 2023 S. Wang et al.
36. Climate-effective use of straw in the EU bioeconomy—comparing avoided and delayed emissions in the agricultural, energy and construction sectors C. Phan-huy et al.
37. Unveiling critical morphological contributions in Forest vegetation carbon storage: An MSPA and explainable machine learning analysis of Jinhua City, China Z. Yang et al.
38. Demographic shrinkage promotes ecosystem services supply capacity in the karst desertification control S. Zhang et al.
39. Fungal necromass carbon contributes more to POC and MAOC under different forest types of Qinling Mountains L. Lai et al.
40. Explainable multi-sensor geospatial modeling of forest biomass dynamics under compound climate extremes in subtropical monsoon regions B. Xu et al.
41. Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years Y. Chen et al.
42. Exploration of ecosystem asset-economy coupling coordination and its endogenous and exogenous drivers in mountainous regions Y. Huang et al.
43. QRS-Trs: Style Transfer-Based Image-to-Image Translation for Carbon Stock Estimation in Quantitative Remote Sensing Z. Yu et al.
44. Dynamics of China's forest carbon storage: the first 30 m annual aboveground biomass mapping from 1985 to 2023 Y. Cai et al.
45. Exploring the utility of remote sensing technology in vegetation below ground biomass (BGB) estimation: a critical review of methods and challenges C. Dlamini et al.
46. Recent gains in global terrestrial carbon stocks are mostly stored in nonliving pools Y. Bar-On et al.
47. Comparison of the CASA and InVEST models’ effects for estimating spatiotemporal differences in carbon storage of green spaces in megacities R. Wang et al.
48. Assessing the impact of land use and cover change on above-ground carbon storage in subtropical forests: a case study of Zhejiang Province, China Z. Huang et al.
49. Spatial Imbalance Patterns of Forest Carbon Density and Their Driving Mechanisms in the Xiuhe River Basin D. Zha et al.
50. Net Primary Productivity Is Driven by Aridity Index and Phenological Phase in Forest Region of China Q. Cui et al.
51. Trend and drivers of forest biomass change in the Greater Bay Area of China from 2000 to 2022 W. Li et al.
52. How to Simulate Carbon Sequestration Potential of Forest Vegetation? A Forest Carbon Sequestration Model across a Typical Mountain City in China D. Guan et al.
53. Evaluating the Research Status of the Remote Sensing-Mediated Monitoring of Forest Biomass: A Bibliometric Analysis of WOS Y. Shi et al.
54. Time-series reconstruction and mapping of forest aboveground biomass in the Great Xing’an Mountains of China using GEDI, MODIS, and machine learning C. Yang et al.
55. Spatial and Temporal Patterns of Forest Biomass Carbon Sink in China from 1990 to 2021 W. Guo et al.
56. Future Reductions in Suitable Habitat for Key Tree Species Result in Declining Boreal Forest Aboveground Biomass Carbon in China B. Zhu et al.
57. The contribution of planted forests to regional carbon storage: evidence from western Hunan, China (1990–2020) T. Deng & Y. Zhu
58. Satellite-based monitoring of China's above-ground biomass carbon sink from 2015 to 2021 H. Fang et al.
59. Influence of BRDF Models and Solar Zenith Angles on Forest Above-Ground Biomass Derived from MODIS Multi-Angular Indices L. Cui et al.
60. Three decades of spatiotemporal dynamics in forest biomass density in the Qinba Mountains J. Chang & C. Huang
61. Mapping Wind Turbine Distribution in Forest Areas of China Using Deep Learning Methods P. Yang et al.
62. Improving estimation of aboveground carbon, nitrogen and phosphorus stock in forest ecosystems by integrating radar and optical remote sensing Q. Liu et al.
63. Drivers of aboveground biomass in Quercus wutaishanica Mayr forests based on random forest and structural equation modeling: A cross-scale analysis S. Ma et al.
64. High-Resolution Mapping and Impact Assessment of Forest Aboveground Carbon Stock in the Pinglu Canal Basin: A Multi-Sensor and Multi-Model Machine Learning Approach W. Xu et al.
65. From Ecological Function to Economic Value: Forest Carbon Sinks and Regional Sustainable Growth in China X. Zhang et al.
66. Identification of degradation risk areas and delineation of key ecological function areas in Qinling region X. Xu et al.