427 citations as recorded by crossref.

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20. Ecosystem CO2 Exchange and Its Economic Implications in Northern Permafrost Regions in the 21st Century C. Mu et al. 10.1029/2023GB007750
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26. 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
27. VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing B. Wild et al. 10.5194/essd-14-1063-2022
28. High‐Resolution Global Contiguous SIF of OCO‐2 L. Yu et al. 10.1029/2018GL081109
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30. Assessing the Reliability of Global Carbon Flux Dataset Compared to Existing Datasets and Their Spatiotemporal Characteristics Z. Xiong et al. 10.3390/cli11100205
31. Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020) J. Liu et al. 10.5194/essd-13-299-2021
32. From Canopy‐Leaving to Total Canopy Far‐Red Fluorescence Emission for Remote Sensing of Photosynthesis: First Results From TROPOMI Z. Zhang et al. 10.1029/2019GL084832
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34. Can we trust remote sensing evapotranspiration products over Africa? I. Weerasinghe et al. 10.5194/hess-24-1565-2020
35. Isolating the Impacts of Land Use/Cover Change and Climate Change on the GPP in the Heihe River Basin of China N. You et al. 10.1029/2020JG005734
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43. Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling A. Chen et al. 10.1029/2022MS003135
44. Satellite-based solar-induced fluorescence tracks seasonal and elevational patterns of photosynthesis in California’s Sierra Nevada mountains L. Kunik et al. 10.1088/1748-9326/ad07b4
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46. Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System B. Raczka et al. 10.1029/2020MS002421
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48. Estimation of China’s terrestrial ecosystem carbon sink: Methods, progress and prospects S. Piao et al. 10.1007/s11430-021-9892-6
49. Integration of flux footprint and physical mechanism into convolutional neural network model for enhanced simulation of urban evapotranspiration H. Chen et al. 10.1016/j.jhydrol.2022.129016
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51. Uncertainty analysis of multiple global GPP datasets in characterizing the lagged effect of drought on photosynthesis X. Xie et al. 10.1016/j.ecolind.2020.106224
52. Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink? C. Seiler et al. 10.1029/2021MS002946
53. MODIS-based modeling of evapotranspiration from woody vegetation supported by root-zone water storage G. Cui et al. 10.1016/j.rse.2024.114000
54. Machine learning models inaccurately predict current and future high-latitude C balances I. Shirley et al. 10.1088/1748-9326/acacb2
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56. Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1 A. Druel et al. 10.5194/gmd-15-8453-2022
57. Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data J. Bonsoms & G. Boulet 10.3390/rs14081788
58. Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms Y. Yao et al. 10.1016/j.agrformet.2017.04.011
59. Inner Mongolia grasslands act as a weak regional carbon sink: A new estimation based on upscaling eddy covariance observations C. You et al. 10.1016/j.agrformet.2023.109719
60. Simulating Erosion‐Induced Soil and Carbon Delivery From Uplands to Rivers in a Global Land Surface Model H. Zhang et al. 10.1029/2020MS002121
61. Spatiotemporal Changes and Driver Analysis of Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands W. Liu et al. 10.3390/rs14153563
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64. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach M. Jung et al. 10.5194/bg-17-1343-2020
65. Upscaling of Latent Heat Flux in Heihe River Basin Based on Transfer Learning Model J. Lin et al. 10.3390/rs15071901
66. Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network J. Beringer et al. 10.1111/gcb.16141
67. Quantifying the effect of forest age in annual net forest carbon balance S. Besnard et al. 10.1088/1748-9326/aaeaeb
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69. 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
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73. The Impact of the 20–50-Day Atmospheric Intraseasonal Oscillation on the Gross Primary Productivity between the Yangtze and Yellow Rivers J. Li et al. 10.1175/JCLI-D-19-0575.1
74. An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East K. Alsafadi et al. 10.1088/1748-9326/ac4765
75. Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review L. Spafford & A. MacDougall 10.5194/gmd-14-5863-2021
76. Revisiting and attributing the global controls over terrestrial ecosystem functions of climate and plant traits at FLUXNET sites via causal graphical models H. Shi et al. 10.5194/bg-20-2727-2023
77. Satellite-based reflectances capture large fraction of variability in global gross primary production (GPP) at weekly time scales J. Joiner & Y. Yoshida 10.1016/j.agrformet.2020.108092
78. A unified vegetation index for quantifying the terrestrial biosphere G. Camps-Valls et al. 10.1126/sciadv.abc7447
79. Improved Estimation of the Gross Primary Production of Europe by Considering the Spatial and Temporal Changes in Photosynthetic Capacity from 2001 to 2016 Q. Wu et al. 10.3390/rs15051172
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81. Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model N. Huang et al. 10.1109/JSTARS.2021.3114190
82. Assimilation of vegetation optical depth retrievals from passive microwave radiometry S. Kumar et al. 10.5194/hess-24-3431-2020
83. A fine spatial resolution estimation scheme for large-scale gross primary productivity (GPP) in mountain ecosystems by integrating an eco-hydrological model with the combination of linear and non-linear downscaling processes X. Xie et al. 10.1016/j.jhydrol.2022.128833
84. Understanding the diurnal cycle of land–atmosphere interactions from flux site observations E. Seo & P. Dirmeyer 10.5194/hess-26-5411-2022
85. Machine learning approach to predict terrestrial gross primary productivity using topographical and remote sensing data D. Prakash Sarkar et al. 10.1016/j.ecoinf.2022.101697
86. Cropland Net Ecosystem Exchange Estimation for the Inland Pampas (Argentina) Using EVI, Land Cover Maps, and Eddy Covariance Fluxes U. Marconato et al. 10.3389/fsoil.2022.903544
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89. Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis H. Shi et al. 10.5194/hess-26-4603-2022
90. Revisiting the choice of the driving temperature for eddy covariance CO2 flux partitioning G. Wohlfahrt & M. Galvagno 10.1016/j.agrformet.2017.02.012
91. Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange M. Kämäräinen et al. 10.5194/bg-20-897-2023
92. Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere W. Li et al. 10.1016/j.ecolind.2021.108126
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