79 citations as recorded by crossref.

1. Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance A. Norton et al. 10.5194/bg-20-2455-2023
2. 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. 10.3389/fclim.2022.941900
3. Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. 10.1029/2020RG000715
4. Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield A. Revill et al. 10.3390/agronomy11020314
5. 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. 10.1175/JCLI-D-17-0380.1
6. Fire decline in dry tropical ecosystems enhances decadal land carbon sink Y. Yin et al. 10.1038/s41467-020-15852-2
7. Rainfall erosion of mountain environment and people flow planning of Leshan giant Buddha site based on big data GIS B. Zhu & J. Zhou 10.1007/s11600-022-00803-w
8. Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application S. Delahaies et al. 10.5194/gmd-10-2635-2017
9. Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties J. Exbrayat et al. 10.5194/esd-9-153-2018
10. 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. 10.3832/efor2368-014
11. Environment-sensitivity functions for gross primary productivity in light use efficiency models S. Bao et al. 10.1016/j.agrformet.2021.108708
12. Plant Traits are Key Determinants in Buffering the Meteorological Sensitivity of Net Carbon Exchanges of Arctic Tundra E. López‐Blanco et al. 10.1029/2018JG004386
13. Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO2 and a Gradient of Experimental Warming J. Jiang et al. 10.1002/2017JG004040
14. Dominant regions and drivers of the variability of the global land carbon sink across timescales X. Zhang et al. 10.1111/gcb.14275
15. Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents K. Yu et al. 10.1111/geb.13736
16. Global evaluation of gross primary productivity in the JULES land surface model v3.4.1 D. Slevin et al. 10.5194/gmd-10-2651-2017
17. Optimizing the Isoprene Emission Model MEGAN With Satellite and Ground‐Based Observational Constraints C. DiMaria et al. 10.1029/2022JD037822
18. Combining European Earth Observation products with Dynamic Global Vegetation Models for estimating Essential Biodiversity Variables M. Dantas de Paula et al. 10.1080/17538947.2019.1597187
19. Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5 A. Fox et al. 10.1029/2018MS001362
20. Parameterizing Lognormal state space models using moment matching J. Smith et al. 10.1007/s10651-023-00570-x
21. Accelerating rates of Arctic carbon cycling revealed by long-term atmospheric CO 2 measurements S. Jeong et al. 10.1126/sciadv.aao1167
22. Global satellite-driven estimates of heterotrophic respiration A. Konings et al. 10.5194/bg-16-2269-2019
23. Assessing Ecosystem State Space Models: Identifiability and Estimation J. Smith et al. 10.1007/s13253-023-00531-8
24. Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system E. López-Blanco et al. 10.5194/esd-10-233-2019
25. Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach L. Hu et al. 10.3390/f11040369
26. Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion P. Rodríguez-Veiga et al. 10.3390/rs12152380
27. Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long‐term data assimilation R. Ge et al. 10.1111/gcb.14547
28. Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network E. White et al. 10.5194/acp-19-4345-2019
29. Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget J. Worden et al. 10.1038/s41467-017-02246-0
30. Inverse Determination of the Influence of Fire on Vegetation Carbon Turnover in the Pantropics J. Exbrayat et al. 10.1029/2018GB005925
31. Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests T. Smallman et al. 10.1002/2016JG003520
32. A mid‐century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity R. Thomas et al. 10.1002/eap.1761
33. The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. 10.5194/gmd-13-55-2020
34. Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century T. Smallman et al. 10.5194/esd-12-1191-2021
35. The value of soil respiration measurements for interpreting and modeling terrestrial carbon cycling C. Phillips et al. 10.1007/s11104-016-3084-x
36. 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. 10.1002/2017JG003760
37. New constraints on biogenic emissions using satellite-based estimates of carbon monoxide fluxes H. Worden et al. 10.5194/acp-19-13569-2019
38. A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0) A. Bloom et al. 10.5194/gmd-10-2141-2017
39. Examining Interactions Between and Among Predictors of Net Ecosystem Exchange: A Machine Learning Approach in a Semi-arid Landscape Q. Zhou et al. 10.1038/s41598-019-38639-y
40. Conditional interval reduction method: A possible new direction for the optimization of process based models R. Hollós et al. 10.1016/j.envsoft.2022.105556
41. The carbon budget of the managed grasslands of Great Britain – informed by earth observations V. Myrgiotis et al. 10.5194/bg-19-4147-2022
42. Response and biophysical regulation of carbon dioxide fluxes to climate variability and anomaly in contrasting ecosystems in northwestern Ohio, USA H. Chu et al. 10.1016/j.agrformet.2016.01.008
43. 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. 10.1016/j.agrformet.2016.07.006
44. Impacts of reduced model complexity and driver resolution on cropland ecosystem photosynthesis estimates A. Revill et al. 10.1016/j.fcr.2015.12.006
45. Scale variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using model–data fusion D. Milodowski et al. 10.5194/bg-20-3301-2023
46. Reference carbon cycle dataset for typical Chinese forests via colocated observations and data assimilation H. He et al. 10.1038/s41597-021-00826-w
47. Lagged effects regulate the inter-annual variability of the tropical carbon balance A. Bloom et al. 10.5194/bg-17-6393-2020
48. Simultaneous state-parameter estimation supports the evaluation of data assimilation performance and measurement design for soil-water-atmosphere-plant system S. Hu et al. 10.1016/j.jhydrol.2017.10.061
49. GOLUM-CNP v1.0: a data-driven modeling of carbon, nitrogen and phosphorus cycles in major terrestrial biomes Y. Wang et al. 10.5194/gmd-11-3903-2018
50. Climate Sensitivities of Carbon Turnover Times in Soil and Vegetation: Understanding Their Effects on Forest Carbon Sequestration R. Ge et al. 10.1029/2020JG005880
51. Predicting the abatement rates of soil organic carbon sequestration management in Western European vineyards using random forest regression F. Payen et al. 10.1016/j.cesys.2021.100024
52. A model-data fusion approach to analyse carbon dynamics in managed grasslands V. Myrgiotis et al. 10.1016/j.agsy.2020.102907
53. 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. 10.1016/j.agrformet.2021.108466
54. Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework S. Stettz et al. 10.5194/bg-19-541-2022
55. Plant Water Uptake Thresholds Inferred From Satellite Soil Moisture M. Bassiouni et al. 10.1029/2020GL087077
56. Predicting lymphatic filariasis elimination in data-limited settings: A reconstructive computational framework for combining data generation and model discovery M. Smith et al. 10.1371/journal.pcbi.1007506
57. From Ecosystem Observation to Environmental Decision-Making: Model-Data Fusion as an Operational Tool T. Smallman et al. 10.3389/ffgc.2021.818661
58. Toward Robust Parameterizations in Ecosystem‐Level Photosynthesis Models S. Bao et al. 10.1029/2022MS003464
59. Assimilation of Remotely Sensed LAI Into CLM4CN Using DART X. Ling et al. 10.1029/2019MS001634
60. Carbon Flux Variability From a Relatively Simple Ecosystem Model With Assimilated Data Is Consistent With Terrestrial Biosphere Model Estimates G. Quetin et al. 10.1029/2019MS001889
61. Detection of hidden model errors by combining single and multi-criteria calibration T. Houska et al. 10.1016/j.scitotenv.2021.146218
62. 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. 10.5194/bg-16-4463-2019
63. CARDAMOM-FluxVal version 1.0: a FLUXNET-based validation system for CARDAMOM carbon and water flux estimates Y. Yang et al. 10.5194/gmd-15-1789-2022
64. Spring photosynthetic onset and net CO2 uptake in Alaska triggered by landscape thawing N. Parazoo et al. 10.1111/gcb.14283
65. Determination of Region of Influence Obtained by Aircraft Vertical Profiles Using the Density of Trajectories from the HYSPLIT Model H. Cassol et al. 10.3390/atmos11101073
66. The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times A. Bloom et al. 10.1073/pnas.1515160113
67. Across‐model spread and shrinking in predicting peatland carbon dynamics under global change E. Hou et al. 10.1111/gcb.16643
68. 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. 10.1029/2021GB007177
69. Leveraging 35 years of <i>Pinus taeda</i> research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments R. Thomas et al. 10.5194/bg-14-3525-2017
70. Consistent assimilation of multiple data streams in a carbon cycle data assimilation system N. MacBean et al. 10.5194/gmd-9-3569-2016
71. Evaluating two land surface models for Brazil using a full carbon cycle benchmark with uncertainties A. Caen et al. 10.1002/cli2.10
72. Challenges in Scaling Up Greenhouse Gas Fluxes: Experience From the UK Greenhouse Gas Emissions and Feedbacks Program P. Levy et al. 10.1029/2021JG006743
73. Constraining a complex biogeochemical model for CO<sub>2</sub> and N<sub>2</sub>O emission simulations from various land uses by model–data fusion T. Houska et al. 10.5194/bg-14-3487-2017
74. 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. 10.1029/2021JG006400
75. 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. 10.1029/2022GB007478
76. Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapotranspiration: ACM-GPP-ETv1 T. Smallman & M. Williams 10.5194/gmd-12-2227-2019
77. Optimal model complexity for terrestrial carbon cycle prediction C. Famiglietti et al. 10.5194/bg-18-2727-2021
78. Remote‐sensing constraints on South America fire traits by Bayesian fusion of atmospheric and surface data A. Bloom et al. 10.1002/2014GL062584
79. Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings M. Kraemer et al. 10.1038/s41598-019-41192-3