92 citations as recorded by crossref.

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26. Soil respiration–driven CO 2 pulses dominate Australia’s flux variability E. Metz et al. 10.1126/science.add7833
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29. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling S. Pan et al. 10.5194/hess-24-1485-2020
30. An improved canopy interception scheme into biogeochemical model for precise simulation of carbon and water fluxes in subtropical coniferous forest F. Zhao et al. 10.1016/j.agrformet.2024.110117
31. Three Gorges Dam Operations Affect the Carbon Dioxide Budget of a Large Downstream Connected Lake X. Zhao et al. 10.1029/2022GL102697
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33. Monitoring tropical forests under a functional perspective with satellite‐based vegetation optical depth G. Vaglio Laurin et al. 10.1111/gcb.15072
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36. Global patterns and ecological implications of diurnal hysteretic response of ecosystem water consumption to vapor pressure deficit X. Xu 10.1016/j.agrformet.2021.108785
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41. Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau K. Shang et al. 10.1016/j.rse.2023.113519
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48. Estimation of Aboveground Vegetation Water Storage in Natural Forests in Jiuzhaigou National Nature Reserve of China Using Machine Learning and the Combination of Landsat 8 and Sentinel-2 Data X. Zhou et al. 10.3390/f13040507
49. Quantifying aboveground vegetation water storage combining Landsat 8 OLI and Sentinel-1 imageries X. Luo et al. 10.1080/10106049.2020.1861662
50. Coupled models of water and carbon cycles from leaf to global: A retrospective and a prospective Y. Wang et al. 10.1016/j.agrformet.2024.110229
51. Actual evapotranspiration estimation over the Tuojiang River Basin based on a hybrid CNN-RF model Y. Li et al. 10.1016/j.jhydrol.2022.127788
52. 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
53. Global modeling diurnal gross primary production from OCO-3 solar-induced chlorophyll fluorescence Z. Zhang et al. 10.1016/j.rse.2022.113383
54. 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
55. Using automated machine learning for the upscaling of gross primary productivity M. Gaber et al. 10.5194/bg-21-2447-2024
56. 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
57. 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
58. Global hunter-gatherer population densities constrained by influence of seasonality on diet composition D. Zhu et al. 10.1038/s41559-021-01548-3
59. A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning W. Shangguan et al. 10.3390/f14050913
60. Approaches to investigate crop responses to ozone pollution: from O3‐FACE to satellite‐enabled modeling C. Montes et al. 10.1111/tpj.15501
61. Linking Land Use Land Cover change to global groundwater storage B. Dasgupta & P. Sanyal 10.1016/j.scitotenv.2022.158618
62. Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality Y. Yang et al. 10.1007/s11427-021-2045-5
63. Upscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning O. Reitz et al. 10.1029/2020JG005814
64. Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century T. Smallman et al. 10.5194/esd-12-1191-2021
65. Synergy between TROPOMI sun-induced chlorophyll fluorescence and MODIS spectral reflectance for understanding the dynamics of gross primary productivity at Integrated Carbon Observatory System (ICOS) ecosystem flux sites H. Balde et al. 10.5194/bg-20-1473-2023
66. A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm L. Chao et al. 10.3390/rs13122414
67. Methane emissions from subtropical wetlands: An evaluation of the role of data filtering on annual methane budgets C. Staudhammer et al. 10.1016/j.agrformet.2022.108972
68. Integrated Validation of Coarse Remotely Sensed Evapotranspiration Products over Heterogeneous Land Surfaces Y. Zhang et al. 10.3390/rs14143467
69. Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison G. McNicol et al. 10.1029/2023AV000956
70. Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half J. Liu et al. 10.1111/gcb.15247
71. Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability Y. Zhang et al. 10.3390/rs13071289
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74. Effects of Land Cover Change on Vegetation Carbon Source/Sink in Arid Terrestrial Ecosystems of Northwest China, 2001–2018 H. Tu et al. 10.3390/rs15092471
75. The effects of spatiotemporal patterns of atmospheric CO2 concentration on terrestrial gross primary productivity estimation Z. Sun et al. 10.1007/s10584-020-02903-2
76. Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange M. Kämäräinen et al. 10.5194/bg-20-897-2023
77. Crowd‐sourced plant occurrence data provide a reliable description of macroecological gradients M. Mahecha et al. 10.1111/ecog.05492
78. A framework for constructing machine learning models with feature set optimisation for evapotranspiration partitioning A. Stapleton et al. 10.1016/j.acags.2022.100105
79. 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
80. Global variability in belowground autotrophic respiration in terrestrial ecosystems X. Tang et al. 10.5194/essd-11-1839-2019
81. Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network Z. Xu et al. 10.1016/j.agrformet.2020.108010
82. Net ecosystem exchange (NEE) estimates 2006–2019 over Europe from a pre-operational ensemble-inversion system S. Munassar et al. 10.5194/acp-22-7875-2022
83. Resolve the Clear‐Sky Continuous Diurnal Cycle of High‐Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature J. Wen et al. 10.1029/2022WR032227
84. Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application Z. Zhang et al. 10.1016/j.rse.2023.113699
85. Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model N. Huang et al. 10.1109/JSTARS.2021.3114190
86. A decreasing carbon allocation to belowground autotrophic respiration in global forest ecosystems X. Tang et al. 10.1016/j.scitotenv.2021.149273
87. A Hybrid Model Coupling Physical Constraints and Machine Learning to Estimate Daily Evapotranspiration in the Heihe River Basin X. Li et al. 10.3390/rs16122143
88. What is global photosynthesis? History, uncertainties and opportunities Y. Ryu et al. 10.1016/j.rse.2019.01.016
89. 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
90. Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration R. Khanal et al. 10.3390/atmos12121565
91. Evaluating Different Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale T. Xu et al. 10.1029/2018JD028447
92. Evaluation of prediction and forecasting models for evapotranspiration of agricultural lands in the Midwest U.S A. Talib et al. 10.1016/j.jhydrol.2021.126579