117 citations as recorded by crossref.

1. A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration J. Chen et al. https://doi.org/10.5194/hess-25-6041-2021
2. Re-Evaluating the Value of Ecosystem Based on Carbon Benefit: A Case Study in Chengdu, China M. Dong et al. https://doi.org/10.3390/land12081605
3. Establishment of DIC concentration model and analysis of influencing factors of groundwater in a typical karst area based on machine learning H. Tian et al. https://doi.org/10.1007/s13146-026-01297-z
4. ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station X. Li et al. https://doi.org/10.1016/j.rse.2021.112360
5. Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest J. Zeng et al. https://doi.org/10.1038/s41597-020-00653-5
6. Remote sensing of terrestrial gross primary productivity: a review of advances in theoretical foundation, key parameters and methods W. Zhu et al. https://doi.org/10.1080/15481603.2024.2318846
7. Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration B. Chen et al. https://doi.org/10.3390/rs13204075
8. Advances in the Remote Sensing of Terrestrial Evaporation M. McCabe et al. https://doi.org/10.3390/rs11091138
9. Earth system data cubes unravel global multivariate dynamics M. Mahecha et al. https://doi.org/10.5194/esd-11-201-2020
10. Predicting spatiotemporal variability in radial tree growth at the continental scale with machine learning P. Bodesheim et al. https://doi.org/10.1017/eds.2022.8
11. More enhanced non-growing season methane exchanges under warming on the Qinghai-Tibetan Plateau Z. Liu et al. https://doi.org/10.1016/j.scitotenv.2024.170438
12. Fusion of Five Satellite-Derived Products Using Extremely Randomized Trees to Estimate Terrestrial Latent Heat Flux over Europe K. Shang et al. https://doi.org/10.3390/rs12040687
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14. Evaluation of Six Satellite-Based Terrestrial Latent Heat Flux Products in the Vegetation Dominated Haihe River Basin of North China Y. Li et al. https://doi.org/10.3390/f12121632
15. Improving the ability of solar-induced chlorophyll fluorescence to track gross primary production through differentiating sunlit and shaded leaves Z. Zhang et al. https://doi.org/10.1016/j.agrformet.2023.109658
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17. High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra A. Virkkala et al. https://doi.org/10.5194/bg-21-335-2024
18. Soil respiration–driven CO 2 pulses dominate Australia’s flux variability E. Metz et al. https://doi.org/10.1126/science.add7833
19. Radiance-based NIRv as a proxy for GPP of corn and soybean G. Wu et al. https://doi.org/10.1088/1748-9326/ab65cc
20. More Accurate and Reliable Phenology Retrieval in Southwest China: Multi-Method Comparison and Uncertainty Analysis F. Tang et al. https://doi.org/10.3390/rs17213538
21. An improved canopy interception scheme into biogeochemical model for precise simulation of carbon and water fluxes in subtropical coniferous forest F. Zhao et al. https://doi.org/10.1016/j.agrformet.2024.110117
22. Three Gorges Dam Operations Affect the Carbon Dioxide Budget of a Large Downstream Connected Lake X. Zhao et al. https://doi.org/10.1029/2022GL102697
23. Rising ecosystem water demand exacerbates the lengthening of tropical dry seasons H. Xu et al. https://doi.org/10.1038/s41467-022-31826-y
24. Spatial and temporal patterns of global soil heterotrophic respiration in terrestrial ecosystems X. Tang et al. https://doi.org/10.5194/essd-12-1037-2020
25. Global patterns and ecological implications of diurnal hysteretic response of ecosystem water consumption to vapor pressure deficit X. Xu https://doi.org/10.1016/j.agrformet.2021.108785
26. Tracking global extreme afternoon photosynthesis depression: Frequency, duration, and drivers from satellite-based hourly GPP products J. Lin et al. https://doi.org/10.1016/j.rse.2026.115597
27. Development of a model framework for terrestrial carbon flux prediction: the Regional Carbon and Climate Analytics Tool (RCCAT) applied to non-tidal wetlands A. Brereton et al. https://doi.org/10.5194/gmd-18-8157-2025
28. On the Potential of Sentinel-2 for Estimating Gross Primary Production D. Pabon-Moreno et al. https://doi.org/10.1109/TGRS.2022.3152272
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33. Evolution of long-term global drought during past 70 years based on estimated evaporation using the generalized complementary relationship L. Ye et al. https://doi.org/10.1016/j.jhydrol.2024.132532
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36. Quantifying aboveground vegetation water storage combining Landsat 8 OLI and Sentinel-1 imageries X. Luo et al. https://doi.org/10.1080/10106049.2020.1861662
37. Global 0.1-Degree Monthly Mean Hourly Total Canopy Solar-Induced Chlorophyll Fluorescence Dataset Derived from Random Forest Y. Liu et al. https://doi.org/10.3390/rs17203429
38. Actual evapotranspiration estimation over the Tuojiang River Basin based on a hybrid CNN-RF model Y. Li et al. https://doi.org/10.1016/j.jhydrol.2022.127788
39. Global hunter-gatherer population densities constrained by influence of seasonality on diet composition D. Zhu et al. https://doi.org/10.1038/s41559-021-01548-3
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43. Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality Y. Yang et al. https://doi.org/10.1007/s11427-021-2045-5
44. 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. https://doi.org/10.3390/rs13122414
45. Integrated Validation of Coarse Remotely Sensed Evapotranspiration Products over Heterogeneous Land Surfaces Y. Zhang et al. https://doi.org/10.3390/rs14143467
46. Combined CO2 measurement record indicates Amazon forest carbon uptake is offset by savanna carbon release S. Botía et al. https://doi.org/10.5194/acp-25-6219-2025
47. Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests S. Besnard et al. https://doi.org/10.1371/journal.pone.0211510
48. Effects of Land Cover Change on Vegetation Carbon Source/Sink in Arid Terrestrial Ecosystems of Northwest China, 2001–2018 H. Tu et al. https://doi.org/10.3390/rs15092471
49. Effects of Precipitation Regimes on Ecosystem Respiration in Agricultural Regions of the Southern Tibetan Plateau F. Zhang et al. https://doi.org/10.3390/agriculture16151662
50. The effects of spatiotemporal patterns of atmospheric CO2 concentration on terrestrial gross primary productivity estimation Z. Sun et al. https://doi.org/10.1007/s10584-020-02903-2
51. An Explainable Deep Forest Approach for Modeling Actual Evapotranspiration in Arid Northwest China Using Multisource Data Y. Wu et al. https://doi.org/10.1109/JSTARS.2026.3684842
52. Can Subdaily LST Be Constructed in High-Latitude Regions Using Polar Orbiting Satellites? Z. Liu et al. https://doi.org/10.1109/LGRS.2025.3546797
53. A framework for constructing machine learning models with feature set optimisation for evapotranspiration partitioning A. Stapleton et al. https://doi.org/10.1016/j.acags.2022.100105
54. On the added value of sequential deep learning for the upscaling of evapotranspiration B. Kraft et al. https://doi.org/10.5194/bg-22-3965-2025
55. Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations O. Peltola et al. https://doi.org/10.5194/essd-11-1263-2019
56. Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network Z. Xu et al. https://doi.org/10.1016/j.agrformet.2020.108010
57. Resolve the Clear‐Sky Continuous Diurnal Cycle of High‐Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature J. Wen et al. https://doi.org/10.1029/2022WR032227
58. Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge Y. Kim et al. https://doi.org/10.1371/journal.pone.0328798
59. Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration R. Khanal et al. https://doi.org/10.3390/atmos12121565
60. A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms X. Chen et al. https://doi.org/10.1038/s41597-022-01520-1
61. Constraining a data-driven CO2 flux model by ecosystem and atmospheric observations using atmospheric transport S. Upton et al. https://doi.org/10.5194/acp-26-2561-2026
62. Understanding interactive processes: a review of CO2 flux, evapotranspiration, and energy partitioning under stressful conditions in dry forest and agricultural environments A. da Rosa Ferraz Jardim et al. https://doi.org/10.1007/s10661-022-10339-7
63. Upscaling Evapotranspiration from a Single-Site to Satellite Pixel Scale X. Li et al. https://doi.org/10.3390/rs13204072
64. Ecological restoration and rising CO2enhance the carbon sink, counteracting climate change in northeastern China B. Huang et al. https://doi.org/10.1088/1748-9326/ac3871
65. Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks G. Tramontana et al. https://doi.org/10.1111/gcb.15203
66. Evaluating the ability of deep learning on actual daily evapotranspiration estimation over the heterogeneous surfaces J. Feng et al. https://doi.org/10.1016/j.agwat.2023.108627
67. Technical note: Flagging inconsistencies in flux tower data M. Jung et al. https://doi.org/10.5194/bg-21-1827-2024
68. Multi-scale evaluation of six fused evapotranspiration products over mainland China: Accuracy, consistency and uncertainty D. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.134371
69. Sub-Daily Natural CO2 Flux Simulation Based on Satellite Data: Diurnal and Seasonal Pattern Comparisons to Anthropogenic CO2 Emissions in the Greater Tokyo Area Q. Wang et al. https://doi.org/10.3390/rs13112037
70. Simulating ecosystem water and carbon fluxes by integrating remote sensing indices with multiple machine learning models L. Sha et al. https://doi.org/10.1016/j.agwat.2026.110622
71. Generating Spatially Robust Carbon Budgets From Flux Tower Observations A. Griebel et al. https://doi.org/10.1029/2019GL085942
72. Large increases in methane emissions expected from North America’s largest wetland complex S. Bansal et al. https://doi.org/10.1126/sciadv.ade1112
73. Global synchronous increase in light-saturated and peak vegetation productivity K. Huang & J. Xia https://doi.org/10.1016/j.fmre.2024.09.001
74. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling S. Pan et al. https://doi.org/10.5194/hess-24-1485-2020
75. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach M. Jung et al. https://doi.org/10.5194/bg-17-1343-2020
76. Monitoring tropical forests under a functional perspective with satellite‐based vegetation optical depth G. Vaglio Laurin et al. https://doi.org/10.1111/gcb.15072
77. Spatial-temporal patterns of land surface evapotranspiration from global products R. Tang et al. https://doi.org/10.1016/j.rse.2024.114066
78. H2CM (v1.0): hybrid modeling of global water–carbon cycles constrained by atmospheric and land observations Z. Baghirov et al. https://doi.org/10.5194/gmd-19-4467-2026
79. Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau K. Shang et al. https://doi.org/10.1016/j.rse.2023.113519
80. To what extent does the CO2 diurnal cycle impact flux estimates derived from global and regional inversions? S. Munassar et al. https://doi.org/10.5194/acp-25-639-2025
81. Spatial heterogeneity of agricultural evapotranspiration as quantified by satellite and flux tower sources A. Goodwell et al. https://doi.org/10.1016/j.agrformet.2025.110608
82. Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data Y. Mao et al. https://doi.org/10.1029/2023JD039387
83. Machine learning with SHapley additive exPlanations for evaluating mine truck productivity under real-site weather conditions at varying temporal resolutions C. Fan et al. https://doi.org/10.1080/17480930.2024.2348877
84. New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western U.S. heatwave X. Li et al. https://doi.org/10.1126/sciadv.adi0775
85. 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. https://doi.org/10.3390/f13040507
86. Coupled models of water and carbon cycles from leaf to global: A retrospective and a prospective Y. Wang et al. https://doi.org/10.1016/j.agrformet.2024.110229
87. WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. https://doi.org/10.5194/essd-17-2507-2025
88. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands J. Irvin et al. https://doi.org/10.1016/j.agrformet.2021.108528
89. Global modeling diurnal gross primary production from OCO-3 solar-induced chlorophyll fluorescence Z. Zhang et al. https://doi.org/10.1016/j.rse.2022.113383
90. Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations J. Leng et al. https://doi.org/10.5194/essd-16-1283-2024
91. Using automated machine learning for the upscaling of gross primary productivity M. Gaber et al. https://doi.org/10.5194/bg-21-2447-2024
92. 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. https://doi.org/10.1111/gcb.15905
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95. Estimation of Grassland Latent Heat Flux in Inner Mongolia from a ConvTransformer Deep Learning Model and MODIS Data N. Yang et al. https://doi.org/10.3390/atmos17080800
96. Upscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning O. Reitz et al. https://doi.org/10.1029/2020JG005814
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107. Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange M. Kämäräinen et al. https://doi.org/10.5194/bg-20-897-2023
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111. Net ecosystem exchange (NEE) estimates 2006–2019 over Europe from a pre-operational ensemble-inversion system S. Munassar et al. https://doi.org/10.5194/acp-22-7875-2022
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