74 citations as recorded by crossref.

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2. The long-term impact of transgressing planetary boundaries on biophysical atmosphere–land interactions M. Drüke et al. https://doi.org/10.5194/esd-15-467-2024
3. Enhanced seasonal CO 2 exchange caused by amplified plant productivity in northern ecosystems M. Forkel et al. https://doi.org/10.1126/science.aac4971
4. Evaluating the Interplay Between Biophysical Processes and Leaf Area Changes in Land Surface Models G. Forzieri et al. https://doi.org/10.1002/2018MS001284
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9. A new tropical savanna PFT, variable root growth and fire improve Cerrado vegetation dynamics simulations in a Dynamic Global Vegetation Model J. Schüler et al. https://doi.org/10.5194/bg-23-95-2026
10. Codominant water control on global interannual variability and trends in land surface phenology and greenness M. Forkel et al. https://doi.org/10.1111/gcb.12950
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14. Assimilation of multiple datasets results in large differences in regional- to global-scale NEE and GPP budgets simulated by a terrestrial biosphere model C. Bacour et al. https://doi.org/10.5194/bg-20-1089-2023
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17. Predicting canopy biophysical properties and sensitivity of plant carbon uptake to water limitations with a coupled eco-hydrological framework L. Lowman & A. Barros https://doi.org/10.1016/j.ecolmodel.2018.01.011
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22. 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
23. 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
24. Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple Biosphere Model (SiB4) K. Haynes et al. https://doi.org/10.1029/2018MS001540
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27. Soil organic carbon dynamics from agricultural management practices under climate change T. Herzfeld et al. https://doi.org/10.5194/esd-12-1037-2021
28. Modeling phenological controls on carbon dynamics in dryland sagebrush ecosystems K. Renwick et al. https://doi.org/10.1016/j.agrformet.2019.04.003
29. Addressing biases in Arctic–boreal carbon cycling in the Community Land Model Version 5 L. Birch et al. https://doi.org/10.5194/gmd-14-3361-2021
30. Climate-induced hysteresis of the tropical forest in a fire-enabled Earth system model M. Drüke et al. https://doi.org/10.1140/epjs/s11734-021-00157-2
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33. An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model J. Kolassa et al. https://doi.org/10.1029/2020MS002083
34. Trade‐offs for food production, nature conservation and climate limit the terrestrial carbon dioxide removal potential L. Boysen et al. https://doi.org/10.1111/gcb.13745
35. Modelling the Arctic taiga-tundra ecotone using ALOS PALSAR and optical earth observation data C. Walther et al. https://doi.org/10.1016/j.jag.2019.05.008
36. Using satellite data to improve the leaf phenology of a global terrestrial biosphere model N. MacBean et al. https://doi.org/10.5194/bg-12-7185-2015
37. 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
38. 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
39. Earth beyond six of nine planetary boundaries K. Richardson et al. https://doi.org/10.1126/sciadv.adh2458
40. A novel bias correction methodology for climate impact simulations S. Sippel et al. https://doi.org/10.5194/esd-7-71-2016
41. Modeling leaf senescence of deciduous tree species in Europe Q. Liu et al. https://doi.org/10.1111/gcb.15132
42. The role of cover crops for cropland soil carbon, nitrogen leaching, and agricultural yields – a global simulation study with LPJmL (V. 5.0-tillage-cc) V. Porwollik et al. https://doi.org/10.5194/bg-19-957-2022
43. Contrasting and interacting changes in simulated spring and summer carbon cycle extremes in European ecosystems S. Sippel et al. https://doi.org/10.1088/1748-9326/aa7398
44. Climate-driven land surface phenology advance is overestimated due to ignoring land cover changes Y. Pan et al. https://doi.org/10.1088/1748-9326/acca34
45. Modeling forest plantations for carbon uptake with the LPJmL dynamic global vegetation model M. Braakhekke et al. https://doi.org/10.5194/esd-10-617-2019
46. Impacts devalue the potential of large-scale terrestrial CO 2 removal through biomass plantations L. Boysen et al. https://doi.org/10.1088/1748-9326/11/9/095010
47. Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements L. Oberhagemann et al. https://doi.org/10.5194/gmd-18-2021-2025
48. New data‐driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression K. Ichii et al. https://doi.org/10.1002/2016JG003640
49. Simulating functional diversity of European natural forests along climatic gradients K. Thonicke et al. https://doi.org/10.1111/jbi.13809
50. Soil Respiration Phenology Improves Modeled Phase of Terrestrial net Ecosystem Exchange in Northern Hemisphere K. Endsley et al. https://doi.org/10.1029/2021MS002804
51. BOSSE v1.0: the Biodiversity Observing System Simulation Experiment J. Pacheco-Labrador et al. https://doi.org/10.5194/gmd-18-8401-2025
52. Role of CO2, climate and land use in regulating the seasonal amplitude increase of carbon fluxes in terrestrial ecosystems: a multimodel analysis F. Zhao et al. https://doi.org/10.5194/bg-13-5121-2016
53. Advancing integrated terrestrial nutrient budget modeling: Challenges and opportunities for improvement across space, time, and end-users C. Pasut et al. https://doi.org/10.1016/j.ecoinf.2026.103721
54. Technical and Economic Irrigation Potentials Within Land and Water Boundaries F. Beier et al. https://doi.org/10.1029/2021WR031924
55. Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data R. Ma et al. https://doi.org/10.5194/gmd-15-6637-2022
56. Resistance to high temperatures of apple varieties breeding of I. V. Michurin Federal Scientific Center those are immune to scab N. Saveleva et al. https://doi.org/10.31676/2073-4948-2020-60-68-73
57. Russian forests show strong potential for young forest growth C. Neigh et al. https://doi.org/10.1038/s43247-025-02006-9
58. Process-Based Models of Phenology for Plants and Animals I. Chuine & J. Régnière https://doi.org/10.1146/annurev-ecolsys-110316-022706
59. 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
60. Global cotton production under climate change – Implications for yield and water consumption Y. Jans et al. https://doi.org/10.5194/hess-25-2027-2021
61. Exploring the environmental drivers of vegetation seasonality changes in the northern extratropical latitudes: a quantitative analysis * X. Kong et al. https://doi.org/10.1088/1748-9326/acf728
62. Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology Q. Liu et al. https://doi.org/10.1111/gcb.13311
63. 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
64. Understanding the Land Carbon Cycle with Space Data: Current Status and Prospects J. Exbrayat et al. https://doi.org/10.1007/s10712-019-09506-2
65. A segmentation algorithm for characterizing rise and fall segments in seasonal cycles: an application to XCO2 to estimate benchmarks and assess model bias L. Calle et al. https://doi.org/10.5194/amt-12-2611-2019
66. 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
67. Fire may prevent future Amazon forest recovery after large-scale deforestation M. Drüke et al. https://doi.org/10.1038/s43247-023-00911-5
68. COLUMNAR GROWTH HABIT APPLE RESISTANCE TO LAW TEMPERATURE AT AUTUMN AND WINTER PERIOD A. Zemisov et al. https://doi.org/10.31676/2073-4948-2020-63-70-76
69. LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description S. Schaphoff et al. https://doi.org/10.5194/gmd-11-1343-2018
70. Variability of phenology and fluxes of water and carbon with observed and simulated soil moisture in the Ent Terrestrial Biosphere Model (Ent TBM version 1.0.1.0.0) Y. Kim et al. https://doi.org/10.5194/gmd-8-3837-2015
71. Evaluation of climate‐related carbon turnover processes in global vegetation models for boreal and temperate forests M. Thurner et al. https://doi.org/10.1111/gcb.13660
72. Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0 G. Schürmann et al. https://doi.org/10.5194/gmd-9-2999-2016
73. Physically constrained generative adversarial networks for improving precipitation fields from Earth system models P. Hess et al. https://doi.org/10.1038/s42256-022-00540-1
74. Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere J. Peng et al. https://doi.org/10.1111/gcb.14627