103 citations as recorded by crossref.

1. A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration J. Chen et al. 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. 10.3390/land12081605
3. ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station X. Li et al. 10.1016/j.rse.2021.112360
4. Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest J. Zeng et al. 10.1038/s41597-020-00653-5
5. Remote sensing of terrestrial gross primary productivity: a review of advances in theoretical foundation, key parameters and methods W. Zhu et al. 10.1080/15481603.2024.2318846
6. Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration B. Chen et al. 10.3390/rs13204075
7. Advances in the Remote Sensing of Terrestrial Evaporation M. McCabe et al. 10.3390/rs11091138
8. Earth system data cubes unravel global multivariate dynamics M. Mahecha et al. 10.5194/esd-11-201-2020
9. Predicting spatiotemporal variability in radial tree growth at the continental scale with machine learning P. Bodesheim et al. 10.1017/eds.2022.8
10. More enhanced non-growing season methane exchanges under warming on the Qinghai-Tibetan Plateau Z. Liu et al. 10.1016/j.scitotenv.2024.170438
11. Fusion of Five Satellite-Derived Products Using Extremely Randomized Trees to Estimate Terrestrial Latent Heat Flux over Europe K. Shang et al. 10.3390/rs12040687
12. A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022 Q. Xu et al. 10.1016/j.agrformet.2025.110645
13. Evaluation of Six Satellite-Based Terrestrial Latent Heat Flux Products in the Vegetation Dominated Haihe River Basin of North China Y. Li et al. 10.3390/f12121632
14. Improving the ability of solar-induced chlorophyll fluorescence to track gross primary production through differentiating sunlit and shaded leaves Z. Zhang et al. 10.1016/j.agrformet.2023.109658
15. Estimating Crop Evapotranspiration in Data-Scare Regions: A Comparative Analysis of Eddy Covariance, Empirical and Remote-Sensing Approaches N. Cutting et al. 10.1007/s41101-024-00299-z
16. High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra A. Virkkala et al. 10.5194/bg-21-335-2024
17. Soil respiration–driven CO 2 pulses dominate Australia’s flux variability E. Metz et al. 10.1126/science.add7833
18. Radiance-based NIRv as a proxy for GPP of corn and soybean G. Wu et al. 10.1088/1748-9326/ab65cc
19. 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
20. Three Gorges Dam Operations Affect the Carbon Dioxide Budget of a Large Downstream Connected Lake X. Zhao et al. 10.1029/2022GL102697
21. Rising ecosystem water demand exacerbates the lengthening of tropical dry seasons H. Xu et al. 10.1038/s41467-022-31826-y
22. Spatial and temporal patterns of global soil heterotrophic respiration in terrestrial ecosystems X. Tang et al. 10.5194/essd-12-1037-2020
23. 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
24. On the Potential of Sentinel-2 for Estimating Gross Primary Production D. Pabon-Moreno et al. 10.1109/TGRS.2022.3152272
25. Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement A. Agustí-Panareda et al. 10.1038/s41597-022-01228-2
26. Representativeness of global climate and vegetation by carbon-monitoring networks; implications for estimates of gross and net primary productivity at biome and global levels P. Alton 10.1016/j.agrformet.2020.108017
27. Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties A. Virkkala et al. 10.1111/gcb.15659
28. X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X J. Nelson et al. 10.5194/bg-21-5079-2024
29. Evolution of long-term global drought during past 70 years based on estimated evaporation using the generalized complementary relationship L. Ye et al. 10.1016/j.jhydrol.2024.132532
30. Robust historical evapotranspiration trends across climate regimes S. Hobeichi et al. 10.5194/hess-25-3855-2021
31. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years J. Xiao et al. 10.1016/j.rse.2019.111383
32. Quantifying aboveground vegetation water storage combining Landsat 8 OLI and Sentinel-1 imageries X. Luo et al. 10.1080/10106049.2020.1861662
33. 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
34. Global hunter-gatherer population densities constrained by influence of seasonality on diet composition D. Zhu et al. 10.1038/s41559-021-01548-3
35. Approaches to investigate crop responses to ozone pollution: from O3‐FACE to satellite‐enabled modeling C. Montes et al. 10.1111/tpj.15501
36. Linking Land Use Land Cover change to global groundwater storage B. Dasgupta & P. Sanyal 10.1016/j.scitotenv.2022.158618
37. 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
38. 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
39. Integrated Validation of Coarse Remotely Sensed Evapotranspiration Products over Heterogeneous Land Surfaces Y. Zhang et al. 10.3390/rs14143467
40. Combined CO2 measurement record indicates Amazon forest carbon uptake is offset by savanna carbon release S. Botía et al. 10.5194/acp-25-6219-2025
41. Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests S. Besnard et al. 10.1371/journal.pone.0211510
42. 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
43. 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
44. Can Subdaily LST Be Constructed in High-Latitude Regions Using Polar Orbiting Satellites? Z. Liu et al. 10.1109/LGRS.2025.3546797
45. A framework for constructing machine learning models with feature set optimisation for evapotranspiration partitioning A. Stapleton et al. 10.1016/j.acags.2022.100105
46. 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
47. Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network Z. Xu et al. 10.1016/j.agrformet.2020.108010
48. Resolve the Clear‐Sky Continuous Diurnal Cycle of High‐Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature J. Wen et al. 10.1029/2022WR032227
49. Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge Y. Kim et al. 10.1371/journal.pone.0328798
50. Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration R. Khanal et al. 10.3390/atmos12121565
51. A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms X. Chen et al. 10.1038/s41597-022-01520-1
52. 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. 10.1007/s10661-022-10339-7
53. Upscaling Evapotranspiration from a Single-Site to Satellite Pixel Scale X. Li et al. 10.3390/rs13204072
54. Ecological restoration and rising CO2enhance the carbon sink, counteracting climate change in northeastern China B. Huang et al. 10.1088/1748-9326/ac3871
55. Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks G. Tramontana et al. 10.1111/gcb.15203
56. Evaluating the ability of deep learning on actual daily evapotranspiration estimation over the heterogeneous surfaces J. Feng et al. 10.1016/j.agwat.2023.108627
57. Technical note: Flagging inconsistencies in flux tower data M. Jung et al. 10.5194/bg-21-1827-2024
58. 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. 10.3390/rs13112037
59. Generating Spatially Robust Carbon Budgets From Flux Tower Observations A. Griebel et al. 10.1029/2019GL085942
60. Large increases in methane emissions expected from North America’s largest wetland complex S. Bansal et al. 10.1126/sciadv.ade1112
61. Global synchronous increase in light-saturated and peak vegetation productivity K. Huang & J. Xia 10.1016/j.fmre.2024.09.001
62. 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
63. 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
64. Monitoring tropical forests under a functional perspective with satellite‐based vegetation optical depth G. Vaglio Laurin et al. 10.1111/gcb.15072
65. Spatial-temporal patterns of land surface evapotranspiration from global products R. Tang et al. 10.1016/j.rse.2024.114066
66. Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau K. Shang et al. 10.1016/j.rse.2023.113519
67. To what extent does the CO2 diurnal cycle impact flux estimates derived from global and regional inversions? S. Munassar et al. 10.5194/acp-25-639-2025
68. Spatial heterogeneity of agricultural evapotranspiration as quantified by satellite and flux tower sources A. Goodwell et al. 10.1016/j.agrformet.2025.110608
69. Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data Y. Mao et al. 10.1029/2023JD039387
70. Machine learning with SHapley additive exPlanations for evaluating mine truck productivity under real-site weather conditions at varying temporal resolutions C. Fan et al. 10.1080/17480930.2024.2348877
71. New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western U.S. heatwave X. Li et al. 10.1126/sciadv.adi0775
72. 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
73. 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
74. WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. 10.5194/essd-17-2507-2025
75. 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
76. Global modeling diurnal gross primary production from OCO-3 solar-induced chlorophyll fluorescence Z. Zhang et al. 10.1016/j.rse.2022.113383
77. 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
78. Using automated machine learning for the upscaling of gross primary productivity M. Gaber et al. 10.5194/bg-21-2447-2024
79. 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
80. 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
81. A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning W. Shangguan et al. 10.3390/f14050913
82. Upscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning O. Reitz et al. 10.1029/2020JG005814
83. 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
84. 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
85. 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
86. 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
87. Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half J. Liu et al. 10.1111/gcb.15247
88. Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability Y. Zhang et al. 10.3390/rs13071289
89. Utility-based regression and meta-learning techniques for modeling actual ET: Comparison to (METRIC-EEFLUX) model F. Abu Salem et al. 10.1016/j.aiia.2024.11.001
90. Estimating the methane flux of the Dajiuhu subalpine peatland using machine learning algorithms and the maximal information coefficient technique X. Li et al. 10.1016/j.scitotenv.2024.170241
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. Crowd‐sourced plant occurrence data provide a reliable description of macroecological gradients M. Mahecha et al. 10.1111/ecog.05492
93. Multiscale analysis of existing actual evapotranspiration products over agropastoral Sahel J. Etchanchu et al. 10.1016/j.jhydrol.2024.132585
94. Global variability in belowground autotrophic respiration in terrestrial ecosystems X. Tang et al. 10.5194/essd-11-1839-2019
95. 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
96. Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application Z. Zhang et al. 10.1016/j.rse.2023.113699
97. Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model N. Huang et al. 10.1109/JSTARS.2021.3114190
98. A decreasing carbon allocation to belowground autotrophic respiration in global forest ecosystems X. Tang et al. 10.1016/j.scitotenv.2021.149273
99. 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
100. What is global photosynthesis? History, uncertainties and opportunities Y. Ryu et al. 10.1016/j.rse.2019.01.016
101. 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
102. Evaluating Different Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale T. Xu et al. 10.1029/2018JD028447
103. 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