96 citations as recorded by crossref.

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2. Evaluation of CMIP5 Earth System Models for the Spatial Patterns of Biomass and Soil Carbon Turnover Times and Their Linkage with Climate D. Wu et al. https://doi.org/10.1175/JCLI-D-17-0380.1
3. Fire decline in dry tropical ecosystems enhances decadal land carbon sink Y. Yin et al. https://doi.org/10.1038/s41467-020-15852-2
4. Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application S. Delahaies et al. https://doi.org/10.5194/gmd-10-2635-2017
5. Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties J. Exbrayat et al. https://doi.org/10.5194/esd-9-153-2018
6. Environment-sensitivity functions for gross primary productivity in light use efficiency models S. Bao et al. https://doi.org/10.1016/j.agrformet.2021.108708
7. Plant Traits are Key Determinants in Buffering the Meteorological Sensitivity of Net Carbon Exchanges of Arctic Tundra E. López‐Blanco et al. https://doi.org/10.1029/2018JG004386
8. Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO2 and a Gradient of Experimental Warming J. Jiang et al. https://doi.org/10.1002/2017JG004040
9. Dominant regions and drivers of the variability of the global land carbon sink across timescales X. Zhang et al. https://doi.org/10.1111/gcb.14275
10. Global evaluation of gross primary productivity in the JULES land surface model v3.4.1 D. Slevin et al. https://doi.org/10.5194/gmd-10-2651-2017
11. 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
12. Reduced spatial heterogeneity of day-night temperature variability difference under global changes F. Liu et al. https://doi.org/10.1016/j.gloplacha.2026.105311
13. Parameterizing Lognormal state space models using moment matching J. Smith et al. https://doi.org/10.1007/s10651-023-00570-x
14. Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020 T. Rosan et al. https://doi.org/10.1038/s43247-024-01205-0
15. Accelerating rates of Arctic carbon cycling revealed by long-term atmospheric CO 2 measurements S. Jeong et al. https://doi.org/10.1126/sciadv.aao1167
16. Assessing Ecosystem State Space Models: Identifiability and Estimation J. Smith et al. https://doi.org/10.1007/s13253-023-00531-8
17. Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system E. López-Blanco et al. https://doi.org/10.5194/esd-10-233-2019
18. Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network E. White et al. https://doi.org/10.5194/acp-19-4345-2019
19. Inverse Determination of the Influence of Fire on Vegetation Carbon Turnover in the Pantropics J. Exbrayat et al. https://doi.org/10.1029/2018GB005925
20. Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests T. Smallman et al. https://doi.org/10.1002/2016JG003520
21. The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. https://doi.org/10.5194/gmd-13-55-2020
22. Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century T. Smallman et al. https://doi.org/10.5194/esd-12-1191-2021
23. 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
24. A comparison of carbon emissions perspectives for a single city and insights for data developers and policymakers G. Allan et al. https://doi.org/10.1088/2515-7620/ae5d98
25. New constraints on biogenic emissions using satellite-based estimates of carbon monoxide fluxes H. Worden et al. https://doi.org/10.5194/acp-19-13569-2019
26. Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge K. Wheeler et al. https://doi.org/10.1016/j.agrformet.2023.109810
27. Examining Interactions Between and Among Predictors of Net Ecosystem Exchange: A Machine Learning Approach in a Semi-arid Landscape Q. Zhou et al. https://doi.org/10.1038/s41598-019-38639-y
28. Response and biophysical regulation of carbon dioxide fluxes to climate variability and anomaly in contrasting ecosystems in northwestern Ohio, USA H. Chu et al. https://doi.org/10.1016/j.agrformet.2016.01.008
29. Precipitation–fire functional interactions control biomass stocks and carbon exchanges across the world's largest savanna M. Williams et al. https://doi.org/10.5194/bg-22-1597-2025
30. Impacts of reduced model complexity and driver resolution on cropland ecosystem photosynthesis estimates A. Revill et al. https://doi.org/10.1016/j.fcr.2015.12.006
31. Lagged effects regulate the inter-annual variability of the tropical carbon balance A. Bloom et al. https://doi.org/10.5194/bg-17-6393-2020
32. GOLUM-CNP v1.0: a data-driven modeling of carbon, nitrogen and phosphorus cycles in major terrestrial biomes Y. Wang et al. https://doi.org/10.5194/gmd-11-3903-2018
33. Predicting the abatement rates of soil organic carbon sequestration management in Western European vineyards using random forest regression F. Payen et al. https://doi.org/10.1016/j.cesys.2021.100024
34. Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework S. Stettz et al. https://doi.org/10.5194/bg-19-541-2022
35. Predicting lymphatic filariasis elimination in data-limited settings: A reconstructive computational framework for combining data generation and model discovery M. Smith et al. https://doi.org/10.1371/journal.pcbi.1007506
36. Assimilation of Remotely Sensed LAI Into CLM4CN Using DART X. Ling et al. https://doi.org/10.1029/2019MS001634
37. CARDAMOM-FluxVal version 1.0: a FLUXNET-based validation system for CARDAMOM carbon and water flux estimates Y. Yang et al. https://doi.org/10.5194/gmd-15-1789-2022
38. Spring photosynthetic onset and net CO2 uptake in Alaska triggered by landscape thawing N. Parazoo et al. https://doi.org/10.1111/gcb.14283
39. Across‐model spread and shrinking in predicting peatland carbon dynamics under global change E. Hou et al. https://doi.org/10.1111/gcb.16643
40. Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies With the ORCHIDEE Terrestrial Biosphere Model N. MacBean et al. https://doi.org/10.1029/2021GB007177
41. Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments R. Thomas et al. https://doi.org/10.5194/bg-14-3525-2017
42. Challenges in Scaling Up Greenhouse Gas Fluxes: Experience From the UK Greenhouse Gas Emissions and Feedbacks Program P. Levy et al. https://doi.org/10.1029/2021JG006743
43. Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter‐Annual Variability K. Mahmud et al. https://doi.org/10.1029/2021JG006400
44. Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapotranspiration: ACM-GPP-ETv1 T. Smallman & M. Williams https://doi.org/10.5194/gmd-12-2227-2019
45. A Coupled Model of Hydraulic Eco‐Physiology and Cambial Growth — Accounting for Biophysical Limitations and Phenology Improves Stem Diameter Prediction at High Temporal Resolution C. Liu et al. https://doi.org/10.1111/pce.15239
46. Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance A. Norton et al. https://doi.org/10.5194/bg-20-2455-2023
47. Evaluating carbon and water fluxes and stocks in Brazil under changing climate and refined regional scenarios for changes in land use A. Castro et al. https://doi.org/10.3389/fclim.2022.941900
48. Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. https://doi.org/10.1029/2020RG000715
49. Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield A. Revill et al. https://doi.org/10.3390/agronomy11020314
50. Rainfall erosion of mountain environment and people flow planning of Leshan giant Buddha site based on big data GIS B. Zhu & J. Zhou https://doi.org/10.1007/s11600-022-00803-w
51. Simulation, calibration and validation protocols for the model 3D-CMCC-CNR-FEM: a case study in the Bonis’ watershed (Calabria, Italy) A. Collalti et al. https://doi.org/10.3832/efor2368-014
52. Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents K. Yu et al. https://doi.org/10.1111/geb.13736
53. A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0 W. Knorr et al. https://doi.org/10.5194/gmd-18-2137-2025
54. Global Carbon Budget 2023 P. Friedlingstein et al. https://doi.org/10.5194/essd-15-5301-2023
55. Optimizing the Isoprene Emission Model MEGAN With Satellite and Ground‐Based Observational Constraints C. DiMaria et al. https://doi.org/10.1029/2022JD037822
56. Unrevealing site-dependent relationship between solar-induced chlorophyll fluorescence and gross primary productivity using the terrestrial ecosystem carbon cycle simulator H. Liu et al. https://doi.org/10.1016/j.rse.2025.115052
57. Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5 A. Fox et al. https://doi.org/10.1029/2018MS001362
58. Global satellite-driven estimates of heterotrophic respiration A. Konings et al. https://doi.org/10.5194/bg-16-2269-2019
59. Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach L. Hu et al. https://doi.org/10.3390/f11040369
60. Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion P. Rodríguez-Veiga et al. https://doi.org/10.3390/rs12152380
61. Differentiable land model reveals global environmental controls on latent ecological functions J. Fang et al. https://doi.org/10.1038/s41467-026-73395-4
62. Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long‐term data assimilation R. Ge et al. https://doi.org/10.1111/gcb.14547
63. Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget J. Worden et al. https://doi.org/10.1038/s41467-017-02246-0
64. A mid‐century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity R. Thomas et al. https://doi.org/10.1002/eap.1761
65. Understanding the effect of disturbance from selective felling on the carbon dynamics of a managed woodland by combining observations with model predictions E. Pinnington et al. https://doi.org/10.1002/2017JG003760
66. A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0) A. Bloom et al. https://doi.org/10.5194/gmd-10-2141-2017
67. Conditional interval reduction method: A possible new direction for the optimization of process based models R. Hollós et al. https://doi.org/10.1016/j.envsoft.2022.105556
68. The carbon budget of the managed grasslands of Great Britain – informed by earth observations V. Myrgiotis et al. https://doi.org/10.5194/bg-19-4147-2022
69. Global Carbon Budget 2024 P. Friedlingstein et al. https://doi.org/10.5194/essd-17-965-2025
70. Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using Four-dimensional Variational data assimilation E. Pinnington et al. https://doi.org/10.1016/j.agrformet.2016.07.006
71. Scale variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using model–data fusion D. Milodowski et al. https://doi.org/10.5194/bg-20-3301-2023
72. Reference carbon cycle dataset for typical Chinese forests via colocated observations and data assimilation H. He et al. https://doi.org/10.1038/s41597-021-00826-w
73. Simultaneous state-parameter estimation supports the evaluation of data assimilation performance and measurement design for soil-water-atmosphere-plant system S. Hu et al. https://doi.org/10.1016/j.jhydrol.2017.10.061
74. Climate Sensitivities of Carbon Turnover Times in Soil and Vegetation: Understanding Their Effects on Forest Carbon Sequestration R. Ge et al. https://doi.org/10.1029/2020JG005880
75. A model-data fusion approach to analyse carbon dynamics in managed grasslands V. Myrgiotis et al. https://doi.org/10.1016/j.agsy.2020.102907
76. Inferring management and predicting sub-field scale C dynamics in UK grasslands using biogeochemical modelling and satellite-derived leaf area data V. Myrgiotis et al. https://doi.org/10.1016/j.agrformet.2021.108466
77. Plant Water Uptake Thresholds Inferred From Satellite Soil Moisture M. Bassiouni et al. https://doi.org/10.1029/2020GL087077
78. From Ecosystem Observation to Environmental Decision-Making: Model-Data Fusion as an Operational Tool T. Smallman et al. https://doi.org/10.3389/ffgc.2021.818661
79. Toward Robust Parameterizations in Ecosystem‐Level Photosynthesis Models S. Bao et al. https://doi.org/10.1029/2022MS003464
80. Isolating the effects of land use and functional variation on Yucatán's forest biomass under global change S. George-Chacon et al. https://doi.org/10.3389/ffgc.2023.1204596
81. Carbon Flux Variability From a Relatively Simple Ecosystem Model With Assimilated Data Is Consistent With Terrestrial Biosphere Model Estimates G. Quetin et al. https://doi.org/10.1029/2019MS001889
82. Detection of hidden model errors by combining single and multi-criteria calibration T. Houska et al. https://doi.org/10.1016/j.scitotenv.2021.146218
83. The importance of physiological, structural and trait responses to drought stress in driving spatial and temporal variation in GPP across Amazon forests S. Flack-Prain et al. https://doi.org/10.5194/bg-16-4463-2019
84. A long-term reconstruction of a global photosynthesis proxy over 1982–2023 J. Fang et al. https://doi.org/10.1038/s41597-025-04686-6
85. Greening of a boreal rich fen driven by CO2 fertilisation S. Thayamkottu et al. https://doi.org/10.1016/j.agrformet.2024.110261
86. Determination of Region of Influence Obtained by Aircraft Vertical Profiles Using the Density of Trajectories from the HYSPLIT Model H. Cassol et al. https://doi.org/10.3390/atmos11101073
87. The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times A. Bloom et al. https://doi.org/10.1073/pnas.1515160113
88. Hyperspectral surface reflectance improves GPP estimation in terrestrial biosphere modeling using model-data fusion H. Liu et al. https://doi.org/10.1016/j.rse.2025.114989
89. Water Stress Dominates 21st‐Century Tropical Land Carbon Uptake P. Levine et al. https://doi.org/10.1029/2023GB007702
90. Consistent assimilation of multiple data streams in a carbon cycle data assimilation system N. MacBean et al. https://doi.org/10.5194/gmd-9-3569-2016
91. Evaluating two land surface models for Brazil using a full carbon cycle benchmark with uncertainties A. Caen et al. https://doi.org/10.1002/cli2.10
92. Constraining a complex biogeochemical model for CO2 and N2O emission simulations from various land uses by model–data fusion T. Houska et al. https://doi.org/10.5194/bg-14-3487-2017
93. Global Carbon Budget 2025 P. Friedlingstein et al. https://doi.org/10.5194/essd-18-3211-2026
94. Attributing Past Carbon Fluxes to CO2 and Climate Change: Respiration Response to CO2 Fertilization Shifts Regional Distribution of the Carbon Sink G. Quetin et al. https://doi.org/10.1029/2022GB007478
95. Optimal model complexity for terrestrial carbon cycle prediction C. Famiglietti et al. https://doi.org/10.5194/bg-18-2727-2021
96. A decrease in the age of respired carbon from the terrestrial biosphere and increase in the asymmetry of its distribution C. Sierra et al. https://doi.org/10.1098/rsta.2022.0200