131 citations as recorded by crossref.

1. Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties Z. Zhang et al. https://doi.org/10.5194/bg-13-1387-2016
2. Uncertainty in model parameters and regional carbon fluxes: A model-data fusion approach J. Xiao et al. https://doi.org/10.1016/j.agrformet.2014.01.022
3. Trait-based representation of hydrological functional properties of plants in weather and ecosystem models A. Matheny et al. https://doi.org/10.1016/j.pld.2016.10.001
4. The origin of Asian monsoons: a modelling perspective D. Tardif et al. https://doi.org/10.5194/cp-16-847-2020
5. Investigating Terrestrial Carbon Uptake Over India Using Multimodel Simulations of Gross Primary Productivity and Satellite‐Based Biophysical Product G. Uchale et al. https://doi.org/10.1029/2023JG007468
6. Are Plant Functional Types Fit for Purpose? J. Cranko Page et al. https://doi.org/10.1029/2023GL104962
7. Challenges and opportunities in the use of remote sensing for C3and C4grass species discrimination and mapping C. Adjorlolo et al. https://doi.org/10.2989/10220119.2012.694120
8. Constraining plant functional types in a semi-arid ecosystem with waveform lidar N. Ilangakoon et al. https://doi.org/10.1016/j.rse.2018.02.070
9. A flux tower site attribute dataset intended for land surface modeling J. Shi et al. https://doi.org/10.5194/essd-17-117-2025
10. Recent trends in Inner Asian forest dynamics to temperature and precipitation indicate high sensitivity to climate change B. Poulter et al. https://doi.org/10.1016/j.agrformet.2012.12.006
11. Plant Hydraulic Trait Covariation: A Global Meta-Analysis to Reduce Degrees of Freedom in Trait-Based Hydrologic Models A. Mursinna et al. https://doi.org/10.3390/f9080446
12. Role of forest regrowth in global carbon sink dynamics T. Pugh et al. https://doi.org/10.1073/pnas.1810512116
13. Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model B. Sakschewski et al. https://doi.org/10.1111/gcb.12870
14. Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models P. Horvath et al. https://doi.org/10.5194/bg-18-95-2021
15. Development of high resolution land surface parameters for the Community Land Model Y. Ke et al. https://doi.org/10.5194/gmd-5-1341-2012
16. Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model T. Dinh et al. https://doi.org/10.5194/gmd-17-6725-2024
17. Present‐day and future contribution of climate and fires to vegetation composition in the boreal forest of China C. Wu et al. https://doi.org/10.1002/ecs2.1917
18. Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine R. Srinet et al. https://doi.org/10.1080/01431161.2020.1766147
19. MaxEnt-Based Potential Distribution Mapping and Range Shift under Future Climatic Scenarios for an Alpine Bamboo Thamnocalamus spathiflorus in Northwestern Himalayas R. Meena et al. https://doi.org/10.3390/land13070931
20. Evolutionary history drives organ-specific variability in plant non-structural carbohydrates W. Xu et al. https://doi.org/10.1038/s41559-026-03129-8
21. Loss and recovery of terrestrial carbon sinks induced by 2020 extreme precipitation in the Yangtze River Valley Z. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.134390
22. A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models K. Harper et al. https://doi.org/10.5194/essd-15-1465-2023
23. The ghost of past climate acting on present‐day plant diversity: Lessons from a climate‐based delimitation of the tropical alpine ecosystem M. Kandziora et al. https://doi.org/10.1111/jse.13048
24. 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
25. Advances in crop growth modeling: A review of perennial crop and beneficial soil microorganism approaches L. Ech-Chatir et al. https://doi.org/10.1016/j.agwat.2025.109548
26. A new model of the global biogeochemical cycle of carbonyl sulfide – Part 2: Use of carbonyl sulfide to constrain gross primary productivity in current vegetation models T. Launois et al. https://doi.org/10.5194/acp-15-9285-2015
27. Tropical rainforest response to marine sky brightening climate engineering H. Muri et al. https://doi.org/10.1002/2015GL063363
28. Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes M. Farella et al. https://doi.org/10.1002/ecs2.3992
29. Current estimates of biogenic emissions from eucalypts uncertain for southeast Australia K. Emmerson et al. https://doi.org/10.5194/acp-16-6997-2016
30. MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations J. Nathaniel et al. https://doi.org/10.1038/s41597-023-02349-y
31. A generalised ecohydrological landscape classification for assessing ecosystem risk in Australia due to an altering water regime A. Herr et al. https://doi.org/10.5194/hess-28-1957-2024
32. A global Fine‐Root Ecology Database to address below‐ground challenges in plant ecology C. Iversen et al. https://doi.org/10.1111/nph.14486
33. Using atmospheric trace gas vertical profiles to evaluate model fluxes: a case study of Arctic-CAP observations and GEOS simulations for the ABoVE domain C. Sweeney et al. https://doi.org/10.5194/acp-22-6347-2022
34. Present and future Köppen-Geiger climate classification maps at 1-km resolution H. Beck et al. https://doi.org/10.1038/sdata.2018.214
35. Land management and climate change determine second‐generation bioenergy potential of the US Northern Great Plains K. Dolan et al. https://doi.org/10.1111/gcbb.12686
36. Process diagnostics of snowmelt runoff in global hydrological and land surface models – Part 1: A systematic evaluation across basins of increasing complexity X. Lei et al. https://doi.org/10.5194/hess-30-5343-2026
37. A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes K. Naudts et al. https://doi.org/10.5194/gmd-8-2035-2015
38. 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
39. El Niño Southern oscillation events contribute significantly to the interannual variations of dust activity over East Asia R. Zhao et al. https://doi.org/10.1016/j.atmosres.2024.107846
40. Towards a “periodic table” of abiotic stress tolerance strategies of woody plants G. Puglielli et al. https://doi.org/10.1016/j.flora.2022.152089
41. Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada L. Wang et al. https://doi.org/10.3390/rs11192286
42. Fitness for future: eLTER RI’s representation of climate and land use change T. Ohnemus et al. https://doi.org/10.1016/j.ecolind.2025.113159
43. Bark beetles as microclimate engineers – thermal characteristics of infested spruce trees at the canopy surface and below the canopy C. Greiser et al. https://doi.org/10.1016/j.agrformet.2025.110796
44. Climate response to Nature Future scenarios in a regional Earth System Model P. Sieber et al. https://doi.org/10.1038/s41467-026-70284-8
45. Antarctic moss stress assessment based on chlorophyll content and leaf density retrieved from imaging spectroscopy data Z. Malenovský et al. https://doi.org/10.1111/nph.13524
46. Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model L. Wang et al. https://doi.org/10.5194/bg-20-2265-2023
47. Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion Y. Liu et al. https://doi.org/10.5194/hess-25-2399-2021
48. If the Yedoma thaws, will we notice? Quantifying detection limits of top-down methane monitoring infrastructures M. Pallandt et al. https://doi.org/10.5194/amt-18-7053-2025
49. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands M. Brandt et al. https://doi.org/10.1038/s41559-018-0530-6
50. The compact Earth system model OSCAR v2.2: description and first results T. Gasser et al. https://doi.org/10.5194/gmd-10-271-2017
51. Consequences of a Reduced Number of Plant Functional Types for the Simulation of Forest Productivity R. Fischer et al. https://doi.org/10.3390/f9080460
52. Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence T. Mukunga et al. https://doi.org/10.3390/fire6050197
53. Urgency of controlling agricultural nitrogen sources to alleviate summertime air pollution in the North China Plain R. Wang et al. https://doi.org/10.1016/j.chemosphere.2022.137124
54. TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description I. Maréchaux et al. https://doi.org/10.5194/gmd-18-5143-2025
55. Sensitivity of a mechanistic photosynthesis model to tropical Andean species and environments S. González-Caro et al. https://doi.org/10.5194/bg-23-3347-2026
56. ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks G. Krinner et al. https://doi.org/10.5194/gmd-11-5027-2018
57. Urban environment as an independent predictor of insulin resistance in a South Asian population M. Thanikachalam et al. https://doi.org/10.1186/s12942-019-0169-9
58. Evolving Climate Classification of East and Central Oromia in a Changing Climate Using the Koppen–Geiger Method G. Balcha & A. Wami https://doi.org/10.11648/j.ijsge.20261501.16
59. Modeling the Terrestrial Biosphere J. Fisher et al. https://doi.org/10.1146/annurev-environ-012913-093456
60. Characterizing uncertainties in the ESA-CCI land cover map of the epoch 2010 and their impacts on MPI-ESM climate simulations G. Georgievski & S. Hagemann https://doi.org/10.1007/s00704-018-2675-2
61. Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: a global assessment F. Albanito et al. https://doi.org/10.1111/gcbb.12242
62. Cropland nitrogen dioxide emissions and effects on the ozone pollution in the North China plain R. Wang et al. https://doi.org/10.1016/j.envpol.2021.118617
63. 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
64. The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use F. Souty et al. https://doi.org/10.5194/gmd-5-1297-2012
65. From plant functional types to plant functional traits Y. Yang et al. https://doi.org/10.1177/0309133315582018
66. Representation of Plant Hydraulics in the Noah‐MP Land Surface Model: Model Development and Multiscale Evaluation L. Li et al. https://doi.org/10.1029/2020MS002214
67. Update of the Köppen–Geiger climate classification for EFSA PLH risk assessment E. Rossi et al. https://doi.org/10.2903/j.efsa.2026.9859
68. Diurnal and seasonal coupling of conifer sap flow and vapour pressure deficit across topoclimatic gradients in a subalpine catchment N. Looker et al. https://doi.org/10.1002/eco.1994
69. Assessing disagreement and tolerance of misclassification of satellite-derived land cover products used in WRF model applications H. Gao & G. Jia https://doi.org/10.1007/s00376-012-2037-4
70. A comparison of the climate and carbon cycle effects of carbon removal by afforestation and an equivalent reduction in fossil fuel emissions K. Jayakrishnan & G. Bala https://doi.org/10.5194/bg-20-1863-2023
71. Estimation of gross primary production in China (1982–2010) with multiple ecosystem models X. Li et al. https://doi.org/10.1016/j.ecolmodel.2015.12.019
72. Wood‐density has no effect on stomatal control of leaf‐level water use efficiency in an Amazonian forest J. Lamour et al. https://doi.org/10.1111/pce.14704
73. Contrasting strategies of hydraulic control in two codominant temperate tree species A. Matheny et al. https://doi.org/10.1002/eco.1815
74. Evaluation of MEGAN predicted biogenic isoprene emissions at urban locations in Southeast Texas S. Kota et al. https://doi.org/10.1016/j.atmosenv.2015.03.027
75. High-resolution land use and land cover dataset for regional climate modelling: a plant functional type map for Europe 2015 V. Reinhart et al. https://doi.org/10.5194/essd-14-1735-2022
76. Influence of high-latitude warming and land-use changes in the early 20th century northern Eurasian CO2sink A. Bastos et al. https://doi.org/10.1088/1748-9326/aac4d3
77. Emerging role of wetland methane emissions in driving 21st century climate change Z. Zhang et al. https://doi.org/10.1073/pnas.1618765114
78. Plant carbon metabolism and climate change: elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration M. Dusenge et al. https://doi.org/10.1111/nph.15283
79. Simulating environmentally‐sensitive tree recruitment in vegetation demographic models A. Hanbury‐Brown et al. https://doi.org/10.1111/nph.18059
80. Constraining a land cover map with satellite-based aboveground biomass estimates over Africa G. Marie et al. https://doi.org/10.5194/gmd-15-2599-2022
81. Bayesian Analysis of Uncertainty in the GlobCover 2009 Land Cover Product at Climate Model Grid Scale T. Quaife & E. Cripps https://doi.org/10.3390/rs8040314
82. Observed and projected changes in global climate zones based on Köppen climate classification D. Cui et al. https://doi.org/10.1002/wcc.701
83. Global evaluation of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 (r5986) Y. Sun et al. https://doi.org/10.5194/gmd-14-1987-2021
84. Sensitivity of global terrestrial carbon cycle dynamics to variability in satellite‐observed burned area B. Poulter et al. https://doi.org/10.1002/2013GB004655
85. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission L. Duncanson et al. https://doi.org/10.1016/j.rse.2021.112845
86. Evaluation of vegetation cover and land‐surface albedo in MPI‐ESM CMIP5 simulations V. Brovkin et al. https://doi.org/10.1029/2012MS000169
87. Global Spatial–Temporal Variability in Terrestrial Productivity and Phenology Regimes between 2000 and 2012 S. Thompson et al. https://doi.org/10.1080/24694452.2017.1309964
88. Generation of a global fuel data set using the Fuel Characteristic Classification System M. Pettinari & E. Chuvieco https://doi.org/10.5194/bg-13-2061-2016
89. Sand and Dust Storm Risk Assessment in Arid Central Asia: Implications for the Environment, Society, and Agriculture W. Wang et al. https://doi.org/10.1007/s13753-024-00591-5
90. Assessing the frequency and drivers of early‐greening in broad‐leaved woodlands along a latitudinal gradient in southern Africa M. Whitecross et al. https://doi.org/10.1111/aec.12448
91. Major forest changes and land cover transitions based on plant functional types derived from the ESA CCI Land Cover product W. Li et al. https://doi.org/10.1016/j.jag.2015.12.006
92. Determination of rice paddy parameters in the global gross primary production capacity estimation algorithm using 6 years of JP-MSE flux observation data K. Muramatsu et al. https://doi.org/10.2480/agrmet.D-16-00017
93. Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere C. Yang et al. https://doi.org/10.1038/s41598-018-29227-7
94. FluxnetLSM R package (v1.0): a community tool for processing FLUXNET data for use in land surface modelling A. Ukkola et al. https://doi.org/10.5194/gmd-10-3379-2017
95. Increasing contribution of peatlands to boreal evapotranspiration in a warming climate M. Helbig et al. https://doi.org/10.1038/s41558-020-0763-7
96. Identifying environmental controls on vegetation greenness phenology through model–data integration M. Forkel et al. https://doi.org/10.5194/bg-11-7025-2014
97. Land surface models significantly underestimate the impact of land-use changes on global evapotranspiration Q. Wang et al. https://doi.org/10.1088/1748-9326/ac38db
98. How have past fire disturbances contributed to the current carbon balance of boreal ecosystems? C. Yue et al. https://doi.org/10.5194/bg-13-675-2016
99. Representing dynamic grassland density in the land surface model ORCHIDEE r9010 S. Xu et al. https://doi.org/10.5194/gmd-19-1-2026
100. Mapping the vegetation distribution of the permafrost zone on the Qinghai-Tibet Plateau Z. Wang et al. https://doi.org/10.1007/s11629-015-3485-y
101. ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation M. Guimberteau et al. https://doi.org/10.5194/gmd-11-121-2018
102. A global 500 m dataset of tree crown morphology to advance vegetation modeling and management J. Xiang et al. https://doi.org/10.1016/j.jag.2026.105401
103. Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets Z. Langford et al. https://doi.org/10.3390/rs8090733
104. Global diurnal and nocturnal parameters of stomatal conductance in woody plants and major crops Y. Hoshika et al. https://doi.org/10.1111/geb.12681
105. The biophysical climate mitigation potential of boreal peatlands during the growing season M. Helbig et al. https://doi.org/10.1088/1748-9326/abab34
106. Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach X. Le Roux et al. https://doi.org/10.3389/fmicb.2016.00628
107. Reconstruction of 30 m Land Cover in the Qilian Mountains from 1980 to 1990 Based on Super-Resolution Generative Adversarial Networks X. Wang et al. https://doi.org/10.3390/rs16224252
108. Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management J. Chang et al. https://doi.org/10.5194/bg-13-3757-2016
109. Long Time Series High-Quality and High-Consistency Land Cover Mapping Based on Machine Learning Method at Heihe River Basin B. Zhong et al. https://doi.org/10.3390/rs13081596
110. Responses of terrestrial ecosystem respiration to soil moisture across Australia E. Metz et al. https://doi.org/10.1088/1748-9326/ae0051
111. Current challenges of implementing anthropogenic land-use and land-cover change in models contributing to climate change assessments R. Prestele et al. https://doi.org/10.5194/esd-8-369-2017
112. Biotic interactions with mycorrhizal systems as extended nutrient acquisition strategies shaping forest soil communities and functions T. Netherway et al. https://doi.org/10.1016/j.baae.2020.10.002
113. How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response A. Schweiger et al. https://doi.org/10.1111/2041-210X.12642
114. Orbital variations as a major driver of climate and biome distribution during the greenhouse to icehouse transition D. Tardif et al. https://doi.org/10.1126/sciadv.abh2819
115. Soil respiration–driven CO 2 pulses dominate Australia’s flux variability E. Metz et al. https://doi.org/10.1126/science.add7833
116. Tropical vegetation response to Heinrich Event 1 as simulated with the UVic ESCM and CCSM3 D. Handiani et al. https://doi.org/10.5194/cp-9-1683-2013
117. 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
118. A review of the major drivers of the terrestrial carbon uptake: model-based assessments, consensus, and uncertainties T. Tharammal et al. https://doi.org/10.1088/1748-9326/ab3012
119. A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables D. Cui et al. https://doi.org/10.5194/essd-13-5087-2021
120. Observing terrestrial ecosystems and the carbon cycle from space D. Schimel et al. https://doi.org/10.1111/gcb.12822
121. Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative B. Poulter et al. https://doi.org/10.5194/gmd-8-2315-2015
122. Assessing global land cover reference datasets for different user communities N. Tsendbazar et al. https://doi.org/10.1016/j.isprsjprs.2014.02.008
123. Future of African terrestrial biodiversity and ecosystems under anthropogenic climate change G. Midgley & W. Bond https://doi.org/10.1038/nclimate2753
124. Seasonal and interannual variability in CO2 fluxes in southern Africa seen by GOSAT E. Metz et al. https://doi.org/10.5194/bg-22-555-2025
125. A methodology to derive global maps of leaf traits using remote sensing and climate data Á. Moreno-Martínez et al. https://doi.org/10.1016/j.rse.2018.09.006
126. Do we (need to) care about canopy radiation schemes in DGVMs? Caveats and potential impacts A. Loew et al. https://doi.org/10.5194/bg-11-1873-2014
127. Global patterns of vegetation carbon use efficiency and their climate drivers deduced from MODIS satellite data and process-based models Y. He et al. https://doi.org/10.1016/j.agrformet.2018.03.009
128. The observed and model-simulated response of southern African vegetation to drought S. Lawal et al. https://doi.org/10.1016/j.agrformet.2019.107698
129. Use of various remote sensing land cover products for plant functional type mapping over Siberia C. Ottlé et al. https://doi.org/10.5194/essd-5-331-2013
130. Effects of a remotely sensed land cover dataset with high spatial resolution on the simulation of secondary air pollutants over china using the nested-grid GEOS-chem chemical transport model M. Li et al. https://doi.org/10.1007/s00376-013-2290-1
131. The Terrestrial Biosphere Model Farm J. Fisher et al. https://doi.org/10.1029/2021MS002676