243 citations as recorded by crossref.

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2. Deep Learned Process Parameterizations Provide Better Representations of Turbulent Heat Fluxes in Hydrologic Models A. Bennett & B. Nijssen 10.1029/2020WR029328
3. More severe drought detected by the assimilation of brightness temperature and terrestrial water storage anomalies in Texas during 2010–2013 W. Chen et al. 10.1016/j.jhydrol.2021.126802
4. The potential of remote sensing-based models on global water-use efficiency estimation: An evaluation and intercomparison of an ecosystem model (BESS) and algorithm (MODIS) using site level and upscaled eddy covariance data S. Yang et al. 10.1016/j.agrformet.2020.107959
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10. Satellite Observations of the Contrasting Response of Trees and Grasses to Variations in Water Availability S. Walther et al. 10.1029/2018GL080535
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19. Rapid recovery of net ecosystem production in a semi-arid woodland after a wildfire Q. Sun et al. 10.1016/j.agrformet.2020.108099
20. Response of vegetation carbon uptake to snow-induced phenological and physiological changes across temperate China S. Chen et al. 10.1016/j.scitotenv.2019.07.222
21. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks Y. Zhang et al. 10.5194/bg-15-5779-2018
22. Estimation of Global Grassland Net Ecosystem Carbon Exchange Using a Model Tree Ensemble Approach W. Liang et al. 10.1029/2019JG005034
23. Carbon fluxes and environmental controls across different alpine grassland types on the Tibetan Plateau Y. Wang et al. 10.1016/j.agrformet.2021.108694
24. Studying the impact of biomass burning aerosol radiative and climate effects on the Amazon rainforest productivity with an Earth system model F. Malavelle et al. 10.5194/acp-19-1301-2019
25. Deep learning and process understanding for data-driven Earth system science M. Reichstein et al. 10.1038/s41586-019-0912-1
26. Global Estimates of Marine Gross Primary Production Based on Machine Learning Upscaling of Field Observations Y. Huang et al. 10.1029/2020GB006718
27. The influence of atmospheric intraseasonal oscillations on terrestrial biospheric CO2 fluxes in Southeast China Forest J. Li et al. 10.1007/s00382-021-05702-2
28. Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models Z. Chen et al. 10.1088/1748-9326/abfac1
29. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years J. Xiao et al. 10.1016/j.rse.2019.111383
30. 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
31. Biological factors dominate the interannual variability of evapotranspiration in an irrigated cropland in the North China Plain H. Lei et al. 10.1016/j.agrformet.2018.01.007
32. High‐Resolution Global Contiguous SIF of OCO‐2 L. Yu et al. 10.1029/2018GL081109
33. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017 Y. Zhang et al. 10.1016/j.rse.2018.12.031
34. 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
35. Warmer spring alleviated the impacts of 2018 European summer heatwave and drought on vegetation photosynthesis S. Wang et al. 10.1016/j.agrformet.2020.108195
36. Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020) J. Liu et al. 10.5194/essd-13-299-2021
37. 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
38. Can we trust remote sensing evapotranspiration products over Africa? I. Weerasinghe et al. 10.5194/hess-24-1565-2020
39. 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
40. A framework for estimating actual evapotranspiration at weather stations without flux observations by combining data from MODIS and flux towers through a machine learning approach C. Zhang et al. 10.1016/j.jhydrol.2021.127047
41. Evaluating GPP and Respiration Estimates Over Northern Midlatitude Ecosystems Using Solar-Induced Fluorescence and Atmospheric CO2 Measurements B. Byrne et al. 10.1029/2018JG004472
42. Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden C. Brenner et al. 10.1007/s00506-021-00768-y
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44. Changes in productivity and carbon storage of grasslands in China under future global warming scenarios of 1.5°C and 2°C Z. Wang et al. 10.1093/jpe/rtz024
45. Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms X. Cui et al. 10.1016/j.scitotenv.2021.145130
46. Accounting for forest age in the tile-based dynamic global vegetation model JSBACH4 (4.20p7; git feature/forests) – a land surface model for the ICON-ESM J. Nabel et al. 10.5194/gmd-13-185-2020
47. Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product Z. Duan et al. 10.3390/rs13214229
48. The impact of indicator selection on assessment of global greening B. Qiu et al. 10.1080/15481603.2021.1879494
49. A global moderate resolution dataset of gross primary production of vegetation for 2000–2016 Y. Zhang et al. 10.1038/sdata.2017.165
50. A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1) M. Forkel et al. 10.5194/gmd-10-4443-2017
51. Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements X. Dou et al. 10.3390/su10010203
52. Retrieval of daily gross primary production over Europe and Africa from an ensemble of SEVIRI/MSG products B. Martínez et al. 10.1016/j.jag.2017.10.011
53. CO 2 exchange and evapotranspiration across dryland ecosystems of southwestern North America J. Biederman et al. 10.1111/gcb.13686
54. Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis S. Walther et al. 10.5194/bg-15-6221-2018
55. Impacts of extreme summers on European ecosystems: a comparative analysis of 2003, 2010 and 2018 A. Bastos et al. 10.1098/rstb.2019.0507
56. Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System B. Raczka et al. 10.1029/2020MS002421
57. ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO<sub>2</sub>, water, and energy fluxes on daily to annual scales C. Qiu et al. 10.5194/gmd-11-497-2018
58. Validation of drought indices using environmental indicators: streamflow and carbon flux data U. Bhuyan-Erhardt et al. 10.1016/j.agrformet.2018.11.016
59. Moisture availability mediates the relationship between terrestrial gross primary production and solar‐induced chlorophyll fluorescence: Insights from global‐scale variations A. Chen et al. 10.1111/gcb.15373
60. 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
61. Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data J. Mosre & F. Suárez 10.3390/w13060870
62. Kernel methods and their derivatives: Concept and perspectives for the earth system sciences J. Johnson et al. 10.1371/journal.pone.0235885
63. Global sensitivities of forest carbon changes to environmental conditions S. Besnard et al. 10.1111/gcb.15877
64. 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
65. Benchmarking large-scale evapotranspiration estimates: A perspective from a calibration-free complementary relationship approach and FLUXCOM N. Ma et al. 10.1016/j.jhydrol.2020.125221
66. Simulating Erosion‐Induced Soil and Carbon Delivery From Uplands to Rivers in a Global Land Surface Model H. Zhang et al. 10.1029/2020MS002121
67. Large loss of CO2 in winter observed across the northern permafrost region S. Natali et al. 10.1038/s41558-019-0592-8
68. 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
69. 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
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71. Quantifying the effect of forest age in annual net forest carbon balance S. Besnard et al. 10.1088/1748-9326/aaeaeb
72. Application of machine-learning methods in forest ecology: recent progress and future challenges Z. Liu et al. 10.1139/er-2018-0034
73. Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb) B. Badawy et al. 10.5194/gmd-11-631-2018
74. Detecting drought-induced GPP spatiotemporal variabilities with sun-induced chlorophyll fluorescence during the 2009/2010 droughts in China S. Chen et al. 10.1016/j.ecolind.2020.107092
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77. Carbon Flux Variability From a Relatively Simple Ecosystem Model With Assimilated Data Is Consistent With Terrestrial Biosphere Model Estimates G. Quetin et al. 10.1029/2019MS001889
78. Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model M. Meroni et al. 10.3390/rs11070749
79. 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
80. Upscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning O. Reitz et al. 10.1029/2020JG005814
81. Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review L. Spafford & A. MacDougall 10.5194/gmd-14-5863-2021
82. 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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85. Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model N. Huang et al. 10.1109/JSTARS.2021.3114190
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87. Ground‐Based Multiangle Solar‐Induced Chlorophyll Fluorescence Observation and Angular Normalization for Assessing Crop Productivity Q. Zhang et al. 10.1029/2020JG006082
88. Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project S. Hantson et al. 10.5194/gmd-13-3299-2020
89. Assimilation of vegetation optical depth retrievals from passive microwave radiometry S. Kumar et al. 10.5194/hess-24-3431-2020
90. Spatio-temporal patterns of evapotranspiration based on upscaling eddy covariance measurements in the dryland of the North China Plain B. Fang et al. 10.1016/j.agrformet.2019.107844
91. A Review of Machine Learning Applications in Land Surface Modeling S. Pal & P. Sharma 10.3390/earth2010011
92. A Data‐Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter‐Annual Variability Y. Yao et al. 10.1029/2020GB006918
93. Contributions of climate change, land use change and CO2 to changes in the gross primary productivity of the Tibetan Plateau X. LUO et al. 10.1080/16742834.2020.1695515
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97. Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming I. Ilie et al. 10.5194/gmd-10-3519-2017
98. Technical note: Uncertainties in eddy covariance CO<sub>2</sub> fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches J. Yao et al. 10.5194/acp-21-15589-2021
99. Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data J. Joiner et al. 10.3390/rs10091346
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103. Climate‐Driven Variability and Trends in Plant Productivity Over Recent Decades Based on Three Global Products M. O'Sullivan et al. 10.1029/2020GB006613
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106. Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental United States S. Kumar et al. 10.1175/JHM-D-18-0237.1
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108. Estimation of Turbulent Heat Fluxes and Gross Primary Productivity by Assimilating Land Surface Temperature and Leaf Area Index X. He et al. 10.1029/2020WR028224
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114. The importance of forest structure for carbon fluxes of the Amazon rainforest E. Rödig et al. 10.1088/1748-9326/aabc61
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118. Foliage Senescence as a Key Parameter for Modeling Gross Primary Productivity in a Mediterranean Shrubland A. Cueva et al. 10.1029/2020JG005839
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132. Machine learning models perform better than traditional empirical models for stomatal conductance when applied to multiple tree species across different forest biomes A. Saunders et al. 10.1016/j.tfp.2021.100139
133. Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave M. Flach et al. 10.5194/bg-15-6067-2018
134. Modeled Surface‐Atmosphere Fluxes From Paired Sites in the Upper Great Lakes Region Using Neural Networks D. Reed et al. 10.1029/2021JG006363
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137. Global trends in land-atmosphere CO<SUB>2</SUB> exchange fluxes: an analysis of a flux measurement dataset and comparison with terrestrial model simulations A. ITO 10.2480/agrmet.D-21-00015
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143. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands J. Irvin et al. 10.1016/j.agrformet.2021.108528
144. Joint Gaussian Processes for Biophysical Parameter Retrieval D. Svendsen et al. 10.1109/TGRS.2017.2767205
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156. Numerical modeling of ozone damage to plants and its effects on atmospheric CO2 in China X. Xie et al. 10.1016/j.atmosenv.2019.116970
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175. Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations O. Peltola et al. 10.5194/essd-11-1263-2019
176. Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product P. Bodesheim et al. 10.5194/essd-10-1327-2018
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