61 citations as recorded by crossref.

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2. Regional-scale data assimilation with the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM) over Siberia H. Arakida et al. 10.1186/s40645-021-00443-6
3. Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles F. Wang et al. 10.5194/hess-22-3863-2018
4. Change point estimation of deciduous forest land surface phenology Y. Xie & A. Wilson 10.1016/j.rse.2020.111698
5. Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities W. Smith et al. 10.1016/j.rse.2019.111401
6. Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2) V. Bastrikov et al. 10.5194/gmd-11-4739-2018
7. Interannual land cover and vegetation variability based on remote sensing data in the HTESSEL land surface model: implementation and effects on simulated water dynamics F. van Oorschot et al. 10.5194/esd-14-1239-2023
8. Understanding the Land Carbon Cycle with Space Data: Current Status and Prospects J. Exbrayat et al. 10.1007/s10712-019-09506-2
9. Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes W. Sun et al. 10.1029/2020AV000310
10. El Niño‐Southern Oscillation‐Induced Variability of Terrestrial Gross Primary Production During the Satellite Era Y. Zhang et al. 10.1029/2019JG005117
11. Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. 10.5194/bg-14-3401-2017
12. Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model M. Peaucelle et al. 10.1111/geb.12937
13. Improving Phenology Representation of Deciduous Forests in the Community Land Model: Evaluation and Modification Using Long‐Term Observations in China Y. Lv et al. 10.1029/2023MS003655
14. Evaluating the performance of land surface model ORCHIDEE-CAN v1.0 on water and energy flux estimation with a single- and multi-layer energy budget scheme Y. Chen et al. 10.5194/gmd-9-2951-2016
15. Simulating the onset of spring vegetation growth across the Northern Hemisphere Q. Liu et al. 10.1111/gcb.13954
16. Dynamic global vegetation models underestimate net CO2 flux mean and inter-annual variability in dryland ecosystems N. MacBean et al. 10.1088/1748-9326/ac1a38
17. Green-Up and Brown-Down: Modelling Grassland Foliage Phenology Responses to Soil Moisture Availability J. Yang et al. 10.2139/ssrn.4107047
18. A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes K. Naudts et al. 10.5194/gmd-8-2035-2015
19. The potential benefit of using forest biomass data in addition to carbon and water flux measurements to constrain ecosystem model parameters: Case studies at two temperate forest sites T. Thum et al. 10.1016/j.agrformet.2016.12.004
20. Testing water fluxes and storage from two hydrology configurations within the ORCHIDEE land surface model across US semi-arid sites N. MacBean et al. 10.5194/hess-24-5203-2020
21. Is the annual maximum leaf area index an important driver of water fluxes simulated by a land surface model in temperate forests? A. Maheu et al. 10.1139/cjfr-2020-0126
22. 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
23. Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe S. Scherrer et al. 10.5194/hess-27-4087-2023
24. Parametric Models to Characterize the Phenology of the Lowveld Savanna at Skukuza, South Africa H. De Lemos et al. 10.3390/rs12233927
25. Local-scale evaluation of the simulated interactions between energy, water and vegetation in ISBA, ORCHIDEE and a diagnostic model J. De Pue et al. 10.5194/bg-19-4361-2022
26. An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model J. Kolassa et al. 10.1029/2020MS002083
27. Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations S. Wu et al. 10.1016/j.isprsjprs.2020.10.017
28. Innovative pheno-network model in estimating crop phenological stages with satellite time series C. Diao 10.1016/j.isprsjprs.2019.04.012
29. Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5 A. Fox et al. 10.1029/2018MS001362
30. A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle P. Peylin et al. 10.5194/gmd-9-3321-2016
31. 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
32. Assimilation of Global Satellite Leaf Area Estimates Reduces Modeled Global Carbon Uptake and Energy Loss by Terrestrial Ecosystems A. Fox et al. 10.1029/2022JG006830
33. Using Free Air CO2 Enrichment data to constrain land surface model projections of the terrestrial carbon cycle N. Raoult et al. 10.5194/bg-21-1017-2024
34. OptiSAIL: A system for the simultaneous retrieval of soil, leaf, and canopy parameters and its application to Sentinel-3 Synergy (OLCI+SLSTR) top-of-canopy reflectances S. Blessing et al. 10.1016/j.srs.2024.100148
35. Plant phenology evaluation of CRESCENDO land surface models – Part 1: Start and end of the growing season D. Peano et al. 10.5194/bg-18-2405-2021
36. Adaptation and Validation of a Sentinel-Based Chlorophyll-a Retrieval Software for the Central European Freshwater Lake, Balaton V. Tóth et al. 10.1007/s41064-021-00160-1
37. Consistent assimilation of multiple data streams in a carbon cycle data assimilation system N. MacBean et al. 10.5194/gmd-9-3569-2016
38. Carbonyl sulfide: comparing a mechanistic representation of the vegetation uptake in a land surface model and the leaf relative uptake approach F. Maignan et al. 10.5194/bg-18-2917-2021
39. Impact of leaf area index from various sources on estimating gross primary production in temperate forests using the JULES land surface model H. Lee et al. 10.1016/j.agrformet.2019.107614
40. Spatiotemporal variability of leaf critical senescence age across northern lands and its key drivers X. Xia et al. 10.1016/j.gloplacha.2024.104587
41. A Process‐Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services Z. Niu et al. 10.1029/2020MS002451
42. Climate Warming Does Not Always Extend the Plant Growing Season in Inner Mongolian Grasslands: Evidence From a 30‐Year In Situ Observations at Eight Experimental Sites G. Wang et al. 10.1029/2019JG005137
43. Grassland gross carbon dioxide uptake based on an improved model tree ensemble approach considering human interventions: global estimation and covariation with climate W. Liang et al. 10.1111/gcb.13592
44. Revisit the Performance of MODIS and VIIRS Leaf Area Index Products from the Perspective of Time-Series Stability D. Zou et al. 10.1109/JSTARS.2022.3214224
45. Evaluating the Interplay Between Biophysical Processes and Leaf Area Changes in Land Surface Models G. Forzieri et al. 10.1002/2018MS001284
46. Climatic Warming Increases Spatial Synchrony in Spring Vegetation Phenology Across the Northern Hemisphere Q. Liu et al. 10.1029/2018GL081370
47. Progress and challenges in remotely sensed terrestrial carbon fluxes T. Wang et al. 10.1080/10095020.2024.2336599
48. A Semiempirical Approach for Decomposition of Remotely Sensed Leaf Area Index Into Overstory and Understory Components Over Russian Forests N. Shabanov et al. 10.1109/TGRS.2023.3287075
49. Vegetation baseline phenology from kilometric global LAI satellite products A. Verger et al. 10.1016/j.rse.2016.02.057
50. Leaf phenology paradox: Why warming matters most where it is already warm B. Seyednasrollah et al. 10.1016/j.rse.2018.02.059
51. ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation M. Guimberteau et al. 10.5194/gmd-11-121-2018
52. Modeling Phenology Combining Data Assimilation Techniques and Bioclimatic Indices in a Cabernet Sauvignon Vineyard (Vitis vinifera L.) in Central Chile V. García-Gutiérrez & F. Meza 10.3390/rs15143537
53. Improving Estimates of Gross Primary Productivity by Assimilating Solar‐Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process‐Based SIF Model C. Bacour et al. 10.1029/2019JG005040
54. Green-up and brown-down: Modelling grassland foliage phenology responses to soil moisture availability J. Yang et al. 10.1016/j.agrformet.2022.109252
55. Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations M. Forkel et al. 10.1038/s41598-019-55187-7
56. Sustained Nonphotochemical Quenching Shapes the Seasonal Pattern of Solar‐Induced Fluorescence at a High‐Elevation Evergreen Forest B. Raczka et al. 10.1029/2018JG004883
57. 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. 10.5194/bg-20-1089-2023
58. Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data N. MacBean et al. 10.1038/s41598-018-20024-w
59. 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
60. No Consistent Evidence for Advancing or Delaying Trends in Spring Phenology on the Tibetan Plateau X. Wang et al. 10.1002/2017JG003949
61. Joint assimilation of eddy covariance flux measurements and FAPAR products over temperate forests within a process‐oriented biosphere model C. Bacour et al. 10.1002/2015JG002966