42 citations as recorded by crossref.

1. Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0 W. Li et al. https://doi.org/10.1016/j.accre.2023.02.001
2. Process refinement contributed more than parameter optimization to improve the CoLM's performance in simulating the carbon and water fluxes in a grassland Y. Li et al. https://doi.org/10.1016/j.agrformet.2020.108067
3. Spatio-temporal variability of gross primary productivity in moist and dry deciduous plant functional types of Northwest Himalayan foothills of India using temperature-greenness model R. Srinet et al. https://doi.org/10.1080/10106049.2020.1801855
4. A Practical Algorithm for Correcting Topographical Effects on Global GPP Products X. Xie et al. https://doi.org/10.1029/2023JG007553
5. Using SMOS soil moisture data combining CO2 flask samples to constrain carbon fluxes during 2010–2015 within a Carbon Cycle Data Assimilation System (CCDAS) M. Wu et al. https://doi.org/10.1016/j.rse.2020.111719
6. Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes A. Tai et al. https://doi.org/10.5194/gmd-17-3733-2024
7. The impact of spatiotemporal variability in atmospheric CO2 concentration on global terrestrial carbon fluxes E. Lee et al. https://doi.org/10.5194/bg-15-5635-2018
8. Skillful seasonal prediction of key carbon cycle components: NPP and fire risk P. Bett et al. https://doi.org/10.1088/2515-7620/ab8b29
9. Impact of climate, rising atmospheric carbon dioxide, and other environmental factors on water-use efficiency at multiple land cover types M. Umair et al. https://doi.org/10.1038/s41598-020-68472-7
10. Parallelization of a distributed ecohydrological model N. Liu et al. https://doi.org/10.1016/j.envsoft.2017.11.033
11. Peak growing season patterns and climate extremes-driven responses of gross primary production estimated by satellite and process based models over North America W. He et al. https://doi.org/10.1016/j.agrformet.2020.108292
12. Disentangling climate effects of greenhouse gas emissions and land cover change on future gross primary productivity in the Yellow River basin, China X. Ru et al. https://doi.org/10.1016/j.jenvman.2025.126026
13. Clumped canopy architecture raises global crop yield and reduces N2O emissions Y. Yan et al. https://doi.org/10.1038/s41477-025-02172-w
14. Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area C. Albergel et al. https://doi.org/10.5194/gmd-10-3889-2017
15. 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
16. A review of the global soil property maps for Earth system models Y. Dai et al. https://doi.org/10.5194/soil-5-137-2019
17. Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity K. Xu et al. https://doi.org/10.1186/s40663-020-00239-y
18. A RCM investigation of the influence of vegetation status and runoff scheme on the summer gross primary production of Tropical Africa S. Anwar & I. Diallo https://doi.org/10.1007/s00704-021-03667-0
19. Estimating Global GPP From the Plant Functional Type Perspective Using a Machine Learning Approach R. Guo et al. https://doi.org/10.1029/2022JG007100
20. Model Structure and Climate Data Uncertainty in Historical Simulations of the Terrestrial Carbon Cycle (1850–2014) G. Bonan et al. https://doi.org/10.1029/2019GB006175
21. Towards an ensemble-based evaluation of land surface models in light of uncertain forcings and observations V. Arora et al. https://doi.org/10.5194/bg-20-1313-2023
22. Implementation of Yale Interactive terrestrial Biosphere model v1.0 into GEOS-Chem v12.0.0: a tool for biosphere–chemistry interactions Y. Lei et al. https://doi.org/10.5194/gmd-13-1137-2020
23. A narrower gap of grazing intensity. Reply to Fetzel et al., 2017. Seasonality constrains to livestock grazing intensity J. Irisarri et al. https://doi.org/10.1111/gcb.13800
24. Global vegetation variability and its response to elevated CO2, global warming, and climate variability – a study using the offline SSiB4/TRIFFID model and satellite data Y. Liu et al. https://doi.org/10.5194/esd-10-9-2019
25. A new remote sensing-derived high resolution eco-physiological land use land cover (HR Eco-P LULC) product in support of regional climate modeling over the Indian landscape K. Gupta et al. https://doi.org/10.1016/j.rsase.2026.101988
26. ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better? C. Albergel et al. https://doi.org/10.5194/hess-22-3515-2018
27. Can Gross Primary Productivity Products be effectively evaluated in regions with few observation data? W. Zhang et al. https://doi.org/10.1080/15481603.2023.2213489
28. Comparing assumptions and applications of dynamic vegetation models used in the Arctic-Boreal zone of Alaska and Canada E. Heffernan et al. https://doi.org/10.1088/1748-9326/ad6619
29. Uncertainty analysis of multiple global GPP datasets in characterizing the lagged effect of drought on photosynthesis X. Xie et al. https://doi.org/10.1016/j.ecolind.2020.106224
30. Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest J. Zeng et al. https://doi.org/10.1038/s41597-020-00653-5
31. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017 Y. Zhang et al. https://doi.org/10.1016/j.rse.2018.12.031
32. Development of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere–atmosphere fluxes relevant for ozone air quality J. Lam et al. https://doi.org/10.5194/gmd-16-2323-2023
33. 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
34. Modeling Profiles of Micrometeorological Variables in a Tropical Premontane Rainforest Using Multi‐Layered CLM (CLM‐ML) J. Song et al. https://doi.org/10.1029/2020MS002259
35. Evaluating Cumulative Drought Effect on Global Vegetation Photosynthesis Using Numerous GPP Products C. Wu & T. Wang https://doi.org/10.3389/fenvs.2022.908875
36. 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. https://doi.org/10.1016/j.agrformet.2019.107614
37. Global Evaluation of the Noah‐MP Land Surface Model and Suggestions for Selecting Parameterization Schemes J. Li et al. https://doi.org/10.1029/2021JD035753
38. TAVIs: Topographically Adjusted Vegetation Index for a Reliable Proxy of Gross Primary Productivity in Mountain Ecosystems X. Xie et al. https://doi.org/10.1109/TGRS.2023.3336727
39. Description and evaluation of the JULES-ES set-up for ISIMIP2b C. Mathison et al. https://doi.org/10.5194/gmd-16-4249-2023
40. 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
41. How can biosphere models simulate enough vegetation biomass in the mountains of the western United States? Implications of meteorological forcing H. Duarte et al. https://doi.org/10.1016/j.envsoft.2021.105288
42. Contrasting responses of vegetation productivity to intraseasonal rainfall in Earth system models B. Harris et al. https://doi.org/10.5194/esd-15-1019-2024