113 citations as recorded by crossref.

1. Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. https://doi.org/10.5194/bg-14-3401-2017
2. Reliable, robust and realistic: the three R's of next-generation land-surface modelling I. Prentice et al. https://doi.org/10.5194/acp-15-5987-2015
3. Competing effects of soil fertility and toxicity on tropical greening J. Fisher et al. https://doi.org/10.1038/s41598-020-63589-1
4. How contemporary bioclimatic and human controls change global fire regimes D. Kelley et al. https://doi.org/10.1038/s41558-019-0540-7
5. MODIS Vegetation Continuous Fields tree cover needs calibrating in tropical savannas R. Adzhar et al. https://doi.org/10.5194/bg-19-1377-2022
6. Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data X. Sun et al. https://doi.org/10.1080/10106049.2019.1655799
7. Improving the LPJmL4-SPITFIRE vegetation–fire model for South America using satellite data M. Drüke et al. https://doi.org/10.5194/gmd-12-5029-2019
8. Analysis and Estimation of Net Primary Productivity of Vegetation in Nanjing Using Multi-Sourced Remote Sensing Data G. Yang https://doi.org/10.1109/ACCESS.2022.3162208
9. The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests C. Reyer et al. https://doi.org/10.5194/essd-12-1295-2020
10. Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models L. Teckentrup et al. https://doi.org/10.5194/bg-16-3883-2019
11. Modelling the effects of CO2 on C3 and C4 grass competition during the mid-Pleistocene transition in South Africa M. Ecker et al. https://doi.org/10.1038/s41598-020-72614-2
12. Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project S. Hantson et al. https://doi.org/10.5194/gmd-13-3299-2020
13. Impact of fuel variability on wildfire emission estimates G. Lasslop & S. Kloster https://doi.org/10.1016/j.atmosenv.2015.05.040
14. Reduced global fire activity due to human demography slows global warming by enhanced land carbon uptake C. Wu et al. https://doi.org/10.1073/pnas.2101186119
15. The direct and indirect effects of the environmental factors on global terrestrial gross primary productivity over the past four decades Y. Chen et al. https://doi.org/10.1088/1748-9326/ad107f
16. Mapping future fire probability under climate change: Does vegetation matter? A. Syphard et al. https://doi.org/10.1371/journal.pone.0201680
17. The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation N. Collier et al. https://doi.org/10.1029/2018MS001354
18. Global Modern Charcoal Dataset (GMCD): A tool for exploring proxy-fire linkages and spatial patterns of biomass burning D. Hawthorne et al. https://doi.org/10.1016/j.quaint.2017.03.046
19. Vegetation plays an important role in mediating future water resources A. Ukkola et al. https://doi.org/10.1088/1748-9326/11/9/094022
20. Tropical climate–vegetation–fire relationships: multivariate evaluation of the land surface model JSBACH G. Lasslop et al. https://doi.org/10.5194/bg-15-5969-2018
21. Hedging our bet on forest permanence for the economic viability of climate targets M. Windisch et al. https://doi.org/10.1038/s41467-025-57607-x
22. Identifying environmental controls on vegetation greenness phenology through model–data integration M. Forkel et al. https://doi.org/10.5194/bg-11-7025-2014
23. Attributing human mortality from fire PM2.5 to climate change C. Park et al. https://doi.org/10.1038/s41558-024-02149-1
24. Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model M. Shi et al. https://doi.org/10.1111/gcb.13131
25. Multi-objective assessment of three remote sensing vegetation products for streamflow prediction in a conceptual ecohydrological model B. Naseem et al. https://doi.org/10.1016/j.jhydrol.2016.10.038
26. Including prescribed burning in fire modelling: A case study from the Brazilian Cerrado using the JULES-INFERNO model R. da Veiga et al. https://doi.org/10.1016/j.pyro.2026.100006
27. Improved simulation of fire–vegetation interactions in the Land surface Processes and eXchanges dynamic global vegetation model (LPX-Mv1) D. Kelley et al. https://doi.org/10.5194/gmd-7-2411-2014
28. Coincidences of climate extremes and anomalous vegetation responses: comparing tree ring patterns to simulated productivity A. Rammig et al. https://doi.org/10.5194/bg-12-373-2015
29. Continental-scale quantification of post-fire vegetation greenness recovery in temperate and boreal North America J. Yang et al. https://doi.org/10.1016/j.rse.2017.07.022
30. How the performance of hydrological models relates to credibility of projections under climate change V. Krysanova et al. https://doi.org/10.1080/02626667.2018.1446214
31. The role of residence time in diagnostic models of global carbon storage capacity: model decomposition based on a traceable scheme C. Yizhao et al. https://doi.org/10.1038/srep16155
32. Historical and future fire occurrence (1850 to 2100) simulated in CMIP5 Earth System Models S. Kloster & G. Lasslop https://doi.org/10.1016/j.gloplacha.2016.12.017
33. Impact of climate and socioeconomic changes on fire carbon emissions in the future: Sustainable economic development might decrease future emissions C. Park et al. https://doi.org/10.1016/j.gloenvcha.2023.102667
34. How to map biomes: Quantitative comparison and review of biome‐mapping methods A. Champreux et al. https://doi.org/10.1002/ecm.1615
35. Relating pollen representation to an evolving Amazonian landscape between the last glacial maximum and Late Holocene R. Smith et al. https://doi.org/10.1017/qua.2020.64
36. The value of soil respiration measurements for interpreting and modeling terrestrial carbon cycling C. Phillips et al. https://doi.org/10.1007/s11104-016-3084-x
37. Plant functional types in Earth system models: past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems S. Wullschleger et al. https://doi.org/10.1093/aob/mcu077
38. Forest Biomass and Net Primary Productivity in Southwestern China: A Meta-Analysis Focusing on Environmental Driving Factors L. Liu et al. https://doi.org/10.3390/f7080173
39. CM2Mc-LPJmL v1.0: biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model M. Drüke et al. https://doi.org/10.5194/gmd-14-4117-2021
40. An optimal ensemble of the Noah-MP land surface model for simulating surface heat fluxes over a typical subtropical forest in South China M. Chang et al. https://doi.org/10.1016/j.agrformet.2019.107815
41. Evaluating the Interplay Between Biophysical Processes and Leaf Area Changes in Land Surface Models G. Forzieri et al. https://doi.org/10.1002/2018MS001284
42. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications C. Müller et al. https://doi.org/10.5194/gmd-10-1403-2017
43. Fire, environmental and anthropogenic controls on pantropical tree cover D. Kelley et al. https://doi.org/10.1038/s43247-024-01869-8
44. Photosynthetic productivity and its efficiencies in ISIMIP2a biome models: benchmarking for impact assessment studies A. Ito et al. https://doi.org/10.1088/1748-9326/aa7a19
45. Evaluation of modern and mid-Holocene seasonal precipitation of the Mediterranean and northern Africa in the CMIP5 simulations A. Perez-Sanz et al. https://doi.org/10.5194/cp-10-551-2014
46. A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions S. Lienert & F. Joos https://doi.org/10.5194/bg-15-2909-2018
47. Fire weakens land carbon sinks before 1.5 °C C. Burton et al. https://doi.org/10.1038/s41561-024-01554-7
48. The ESA's Earth Observation Open Science Program [Space Agencies] P. Mathieu et al. https://doi.org/10.1109/MGRS.2017.2688704
49. Causal relationships versus emergent patterns in the global controls of fire frequency I. Bistinas et al. https://doi.org/10.5194/bg-11-5087-2014
50. Environmental drivers of drought deciduous phenology in the Community Land Model K. Dahlin et al. https://doi.org/10.5194/bg-12-5061-2015
51. Model prediction of biome‐specific global soil respiration from 1960 to 2012 Z. Zhao et al. https://doi.org/10.1002/2016EF000480
52. Plant phenology evaluation of CRESCENDO land surface models – Part 1: Start and end of the growing season D. Peano et al. https://doi.org/10.5194/bg-18-2405-2021
53. Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers S. Sippel et al. https://doi.org/10.1371/journal.pone.0164960
54. Pathways to bridge the biophysical realism gap in ecosystem services mapping approaches S. Lavorel et al. https://doi.org/10.1016/j.ecolind.2016.11.015
55. The China plant trait database version 2 H. Wang et al. https://doi.org/10.1038/s41597-022-01884-4
56. A hydroclimatic model for the distribution of fire on Earth M. Boer et al. https://doi.org/10.1088/2515-7620/abec1f
57. Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model R. Son et al. https://doi.org/10.1029/2023MS003710
58. The Terrestrial Biosphere Model Farm J. Fisher et al. https://doi.org/10.1029/2021MS002676
59. Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED) R. Fisher et al. https://doi.org/10.5194/gmd-8-3593-2015
60. Assessment of JSBACHv4.30 as a land component of ICON-ESM-V1 in comparison to its predecessor JSBACHv3.2 of MPI-ESM1.2 R. Schneck et al. https://doi.org/10.5194/gmd-15-8581-2022
61. LPJmL4 – a dynamic global vegetation model with managed land – Part 2: Model evaluation S. Schaphoff et al. https://doi.org/10.5194/gmd-11-1377-2018
62. Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators A. Krause et al. https://doi.org/10.5194/bg-14-4829-2017
63. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions S. Rabin et al. https://doi.org/10.5194/gmd-10-1175-2017
64. Combining European Earth Observation products with Dynamic Global Vegetation Models for estimating Essential Biodiversity Variables M. Dantas de Paula et al. https://doi.org/10.1080/17538947.2019.1597187
65. Adapting a dynamic vegetation model for regional biomass, plant biogeography, and fire modeling in the Greater Yellowstone Ecosystem: Evaluating LPJ-GUESS-LMfireCF K. Emmett et al. https://doi.org/10.1016/j.ecolmodel.2020.109417
66. Evaluation of biospheric components in Earth system models using modern and palaeo-observations: the state-of-the-art A. Foley et al. https://doi.org/10.5194/bg-10-8305-2013
67. Lightning Forcing in Global Fire Models: The Importance of Temporal Resolution A. Felsberg et al. https://doi.org/10.1002/2017JG004080
68. Dry corridors opened by fire and low CO2 in Amazonian rainforest during the Last Glacial Maximum H. Sato et al. https://doi.org/10.1038/s41561-021-00777-2
69. A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1) M. Forkel et al. https://doi.org/10.5194/gmd-10-4443-2017
70. Description and evaluation of the JULES-ES set-up for ISIMIP2b C. Mathison et al. https://doi.org/10.5194/gmd-16-4249-2023
71. Impact of large‐scale climate extremes on biospheric carbon fluxes: An intercomparison based on MsTMIP data J. Zscheischler et al. https://doi.org/10.1002/2014GB004826
72. Including vegetation dynamics in an atmospheric chemistry-enabled general circulation model: linking LPJ-GUESS (v4.0) with the EMAC modelling system (v2.53) M. Forrest et al. https://doi.org/10.5194/gmd-13-1285-2020
73. The status and challenge of global fire modelling S. Hantson et al. https://doi.org/10.5194/bg-13-3359-2016
74. Different trends of vegetation activity over northern extratropics during two multidecadal warming periods in the 20th century Z. Guo et al. https://doi.org/10.1016/j.gloplacha.2023.104181
75. Climate versus carbon dioxide controls on biomass burning: a model analysis of the glacial–interglacial contrast M. Martin Calvo et al. https://doi.org/10.5194/bg-11-6017-2014
76. The importance of data type, laser spot density and modelling method for vegetation height mapping in continental China C. Liu et al. https://doi.org/10.1080/01431161.2016.1252472
77. Climate–vegetation–fire interactions and feedbacks: trivial detail or major barrier to projecting the future of the Earth system? R. Harris et al. https://doi.org/10.1002/wcc.428
78. Pyrogeographic models, feedbacks and the future of global fire regimes D. Bowman et al. https://doi.org/10.1111/geb.12180
79. Influence of global climate modes on wildfire occurrence in the contiguous United States under recent and future climates T. Keeping et al. https://doi.org/10.1007/s00382-025-07998-w
80. An operational framework for biome boundary research with examples from South Africa A. Potts et al. https://doi.org/10.1016/j.sajb.2015.07.002
81. Long‐term terrestrial carbon dynamics in the Midwestern United States during 1850–2015: Roles of land use and cover change and agricultural management Z. Yu et al. https://doi.org/10.1111/gcb.14074
82. Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration I. Fer et al. https://doi.org/10.1111/gcb.15409
83. Global ecosystems and fire: Multi‐model assessment of fire‐induced tree‐cover and carbon storage reduction G. Lasslop et al. https://doi.org/10.1111/gcb.15160
84. Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES) C. Burton et al. https://doi.org/10.5194/gmd-12-179-2019
85. Challenging a Global Land Surface Model in a Local Socio-Environmental System K. Dahlin et al. https://doi.org/10.3390/land9100398
86. Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests B. Sakschewski et al. https://doi.org/10.5194/bg-18-4091-2021
87. Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models M. Forkel et al. https://doi.org/10.5194/bg-16-57-2019
88. Global burned area increasingly explained by climate change C. Burton et al. https://doi.org/10.1038/s41558-024-02140-w
89. Modeling the Terrestrial Biosphere J. Fisher et al. https://doi.org/10.1146/annurev-environ-012913-093456
90. PeatDepth-ML: a global map of peat depth predicted using machine learning J. Skye et al. https://doi.org/10.5194/bg-23-2959-2026
91. Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0) W. von Bloh et al. https://doi.org/10.5194/gmd-11-2789-2018
92. Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests L. Hawkins et al. https://doi.org/10.1029/2018MS001577
93. A global database of net primary production of terrestrial ecosystems M. Rodal et al. https://doi.org/10.1038/s41597-025-05773-4
94. Effects of fire and CO2 on biogeography and primary production in glacial and modern climates M. Martin Calvo & I. Prentice https://doi.org/10.1111/nph.13485
95. Synergy between land use and climate change increases future fire risk in Amazon forests Y. Le Page et al. https://doi.org/10.5194/esd-8-1237-2017
96. Development of a statistical model for global burned area simulation within a DGVM-compatible framework B. Kavhu et al. https://doi.org/10.5194/bg-22-7001-2025
97. Global Evaluation of the Noah‐MP Land Surface Model and Suggestions for Selecting Parameterization Schemes J. Li et al. https://doi.org/10.1029/2021JD035753
98. Wildfires on a changing planet O. Haas et al. https://doi.org/10.1038/s41467-025-68176-4
99. A fire model with distinct crop, pasture, and non-agricultural burning: use of new data and a model-fitting algorithm for FINAL.1 S. Rabin et al. https://doi.org/10.5194/gmd-11-815-2018
100. Biophsyical constraints on gross primary production by the terrestrial biosphere H. Wang et al. https://doi.org/10.5194/bg-11-5987-2014
101. Separation of the Effects of Land and Climate Model Errors on Simulated Contemporary Land Carbon Cycle Trends in the MPI Earth System Model version 1* D. Dalmonech et al. https://doi.org/10.1175/JCLI-D-13-00593.1
102. Identifying uncertainties in scenarios and models of socio-ecological systems in support of decision-making M. Rounsevell et al. https://doi.org/10.1016/j.oneear.2021.06.003
103. Benchmarking the seasonal cycle of CO2 fluxes simulated by terrestrial ecosystem models S. Peng et al. https://doi.org/10.1002/2014GB004931
104. Improving the dynamics of Northern Hemisphere high-latitude vegetation in the ORCHIDEE ecosystem model D. Zhu et al. https://doi.org/10.5194/gmd-8-2263-2015
105. Bimodal fire regimes unveil a global‐scale anthropogenic fingerprint A. Benali et al. https://doi.org/10.1111/geb.12586
106. Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO2 trends Z. Sun et al. https://doi.org/10.1016/j.scitotenv.2019.03.025
107. How Will Deforestation and Vegetation Degradation Affect Global Fire Activity? C. Park et al. https://doi.org/10.1029/2020EF001786
108. Technical note: Low meteorological influence found in 2019 Amazonia fires D. Kelley et al. https://doi.org/10.5194/bg-18-787-2021
109. Niche-dependent forest and savanna fragmentation in Tropical South America during the Last Glacial Maximum D. Kelley et al. https://doi.org/10.1038/s44185-024-00056-4
110. Options to model the effects of tillage on N2O emissions at the global scale F. Lutz et al. https://doi.org/10.1016/j.ecolmodel.2018.11.015
111. Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model M. Forrest et al. https://doi.org/10.5194/bg-21-5539-2024
112. Modelling the response of yields and tissue C : N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe S. Olin et al. https://doi.org/10.5194/bg-12-2489-2015
113. Exploring the spatio-temporal patterns of global mangrove gross primary production and quantifying the factors affecting its estimation, 1996–2020 Z. Sun et al. https://doi.org/10.1016/j.scitotenv.2023.168262