398 citations as recorded by crossref.

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28. Assessing the Reliability of Global Carbon Flux Dataset Compared to Existing Datasets and Their Spatiotemporal Characteristics Z. Xiong et al. 10.3390/cli11100205
29. Impact of Aerosols From Urban and Shipping Emission Sources on Terrestrial Carbon Uptake and Evapotranspiration: A Case Study in East Asia M. Huang et al. 10.1029/2019JD030818
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31. 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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33. Can we trust remote sensing evapotranspiration products over Africa? I. Weerasinghe et al. 10.5194/hess-24-1565-2020
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54. Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data J. Bonsoms & G. Boulet 10.3390/rs14081788
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56. 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
57. Simulating Erosion‐Induced Soil and Carbon Delivery From Uplands to Rivers in a Global Land Surface Model H. Zhang et al. 10.1029/2020MS002121
58. 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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62. Upscaling of Latent Heat Flux in Heihe River Basin Based on Transfer Learning Model J. Lin et al. 10.3390/rs15071901
63. 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
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66. 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
67. Observational Constraints on the Response of High‐Latitude Northern Forests to Warming J. Liu et al. 10.1029/2020AV000228
68. Modeling the daytime net primary productivity of maize at the canopy scale based on UAV multispectral imagery and machine learning M. Peng et al. 10.1016/j.jclepro.2022.133041
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74. 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
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79. Assimilation of vegetation optical depth retrievals from passive microwave radiometry S. Kumar et al. 10.5194/hess-24-3431-2020
80. 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
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83. 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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85. On the relationship between sub-daily instantaneous and daily total gross primary production: Implications for interpreting satellite-based SIF retrievals Y. Zhang et al. 10.1016/j.rse.2017.12.009
86. 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
87. Revisiting the choice of the driving temperature for eddy covariance CO2 flux partitioning G. Wohlfahrt & M. Galvagno 10.1016/j.agrformet.2017.02.012
88. Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange M. Kämäräinen et al. 10.5194/bg-20-897-2023
89. 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
90. Sun-induced fluorescence as a proxy for primary productivity across vegetation types and climates M. Pickering et al. 10.5194/bg-19-4833-2022
91. Gap‐filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis Y. Kim et al. 10.1111/gcb.14845
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350. Bioenergy Crops for Low Warming Targets Require Half of the Present Agricultural Fertilizer Use W. Li et al. 10.1021/acs.est.1c02238
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