405 citations as recorded by crossref.

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34. Incorporation of needleleaf traits improves estimation of light absorption and gross primary production of evergreen needleleaf forests B. Pan et al. 10.1016/j.agrformet.2025.110526
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48. Physics-constrained machine learning for satellite-derived evapotranspiration in China C. Zhang et al. 10.1016/j.jhydrol.2025.133512
49. Dryland Ecosystems of the Sudano-Sahel: A Vegetation Model Perspective W. Verbruggen 10.1163/2031356X-35010012
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54. Soil respiration–driven CO 2 pulses dominate Australia’s flux variability E. Metz et al. 10.1126/science.add7833
55. Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes W. Sun et al. 10.1029/2020AV000310
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57. Terrestrial photosynthesis inferred from plant carbonyl sulfide uptake J. Lai et al. 10.1038/s41586-024-08050-3
58. MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations J. Nathaniel et al. 10.1038/s41597-023-02349-y
59. Causal inference reveals the dominant role of interannual variability of carbon sinks in complicated environmental-terrestrial ecosystems C. Dang et al. 10.1016/j.rse.2024.114300
60. Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations J. Leng et al. 10.5194/essd-16-1283-2024
61. Representation of Leaf‐to‐Canopy Radiative Transfer Processes Improves Simulation of Far‐Red Solar‐Induced Chlorophyll Fluorescence in the Community Land Model Version 5 R. Li et al. 10.1029/2021MS002747
62. Machine learning methods for assessing photosynthetic activity: environmental monitoring applications S. Khruschev et al. 10.1007/s12551-022-00982-2
63. Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning X. Zhu et al. 10.1016/j.scitotenv.2022.159390
64. The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales S. Botía et al. 10.1111/gcb.15905
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66. Vegetation Photosynthesis Model v3.0: Improved Estimates of Terrestrial Gross Primary Production from Individual Eddy Flux Tower Sites to the Globe L. Pan et al. 10.34133/remotesensing.0471
67. Evaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO2 T. Thum et al. 10.5194/bg-17-5721-2020
68. Testing stomatal models at the stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0.1) Y. Wang et al. 10.5194/gmd-14-6741-2021
69. Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling A. van der Woude et al. 10.5194/essd-15-579-2023
70. Emergent Climatic Controls on Soil Carbon Turnover and Its Variability in Warm Climates H. Huang et al. 10.1029/2023GL105291
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73. Machine learning algorithms improve MODIS GPP estimates in United States croplands D. Menefee et al. 10.3389/frsen.2023.1240895
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76. The Diurnal Dynamics of Gross Primary Productivity Using Observations From the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R Series at an Oak Savanna Ecosystem A. Khan et al. 10.1029/2021JG006701
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78. Apparent ecosystem carbon turnover time: uncertainties and robust features N. Fan et al. 10.5194/essd-12-2517-2020
79. Modeled Surface‐Atmosphere Fluxes From Paired Sites in the Upper Great Lakes Region Using Neural Networks D. Reed et al. 10.1029/2021JG006363
80. Bridging Satellite Productivity and Global Biodiversity: Unveiling Insights through Dynamic Habitat Indices K. Liu et al. 10.34133/remotesensing.0624
81. Potential for equation discovery with AI in the climate sciences C. Huntingford et al. 10.5194/esd-16-475-2025
82. CO2 fertilization is spatially distinct from stomatal conductance reduction in controlling ecosystem water-use efficiency increase X. Zhang et al. 10.1088/1748-9326/ac6c9c
83. Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. 10.1088/1748-9326/adad02
84. Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al. 10.1016/j.xinn.2024.100691
85. Remote Sensing‐Based Assessment of Carbon and Water Ecosystem Services in Response to Climate and Vegetation Cover Changes Across Global Major River Basins Y. Wang & J. Jin 10.1002/ldr.70025
86. WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. 10.5194/essd-17-2507-2025
87. Preface: Advancing deep learning for remote sensing time series data analysis H. Zhang et al. 10.1016/j.rse.2025.114711
88. Aircraft Measurements of Tropospheric CO2 in the North China Plain in Autumn and Winter of 2018–2019 H. Zhang et al. 10.3390/atmos14121835
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95. The spatial heterogeneity of the relationship between gross primary production and sun-induced chlorophyll fluorescence regulated by climate conditions during 2007–2018 Y. Wang et al. 10.1016/j.gecco.2021.e01721
96. Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model A. Rahman et al. 10.3389/frwa.2021.789352
97. Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for Hurricane Disturbance and Recovery M. Shi et al. 10.1029/2023MS003679
98. 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
99. The ASK-16 motorized glider: an airborne eddy covariance platform to measure turbulence, energy, and matter fluxes I. Wiekenkamp et al. 10.5194/amt-18-749-2025
100. Estimating carbon fluxes over North America using a physics-constrained deep learning model B. Fan et al. 10.1016/j.isprsjprs.2025.06.033
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117. Non-growing season carbon emissions in a northern peatland are projected to increase under global warming A. Rafat et al. 10.1038/s43247-021-00184-w
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134. Multi-year observations reveal a larger than expected autumn respiration signal across northeast Eurasia B. Byrne et al. 10.5194/bg-19-4779-2022
135. Solar-Induced Chlorophyll Fluorescence (SIF): Towards a Better Understanding of Vegetation Dynamics and Carbon Uptake in Arctic-Boreal Ecosystems R. Cheng 10.1007/s40641-024-00194-8
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