125 citations as recorded by crossref.

1. Evaluation of remotely sensed global evapotranspiration data from inland river basins Z. Liu https://doi.org/10.1002/hyp.14774
2. Soil-Permittivity Estimation Under Grassland Using Machine-Learning and Polarimetric Decomposition Techniques R. Hansch et al. https://doi.org/10.1109/TGRS.2020.3010104
3. An improved light use efficiency model by considering canopy nitrogen concentrations and multiple environmental factors H. Zhang et al. https://doi.org/10.1016/j.agrformet.2023.109359
4. Artificial neural networks estimate evapotranspiration for Miscanthus × giganteus as effectively as empirical model but with fewer inputs G. Aslan-Sungur et al. https://doi.org/10.1007/s00704-025-05812-5
5. Land–atmosphere interactions in the tropics – a review P. Gentine et al. https://doi.org/10.5194/hess-23-4171-2019
6. Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress G. Mohammed et al. https://doi.org/10.1016/j.rse.2019.04.030
7. Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence P. Gentine & S. Alemohammad https://doi.org/10.1002/2017GL076294
8. Partitioning of Historical Precipitation Into Evaporation and Runoff Based on Hydrologic Dynamics Identified With Recent SMAP Satellite Measurements R. Akbar et al. https://doi.org/10.1029/2020WR027307
9. Contrasting responses of vegetation productivity to intraseasonal rainfall in Earth system models B. Harris et al. https://doi.org/10.5194/esd-15-1019-2024
10. Comprehensive analysis of methods for estimating actual paddy evapotranspiration—A review K. Behura et al. https://doi.org/10.3389/frwa.2025.1553732
11. Artificial Neural Network Model of Soil Heat Flux over Multiple Land Covers in South America B. de Andrade et al. https://doi.org/10.3390/rs13122337
12. A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning W. Shangguan et al. https://doi.org/10.3390/f14050913
13. Vegetation Photosynthesis Model v3.0: Improved Estimates of Terrestrial Gross Primary Production from Individual Eddy Flux Tower Sites to the Globe L. Pan et al. https://doi.org/10.34133/remotesensing.0471
14. On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient Y. Bai et al. https://doi.org/10.1016/j.agrformet.2020.108308
15. A new global time-series GPP production: DFRF-GPP Z. Xiufang et al. https://doi.org/10.1016/j.ecolind.2024.111551
16. Multiscale evaluation of three remote sensing-based evapotranspiration models across the humid to arid tropics: a study over India K. Athira et al. https://doi.org/10.1080/02626667.2025.2541749
17. Analysis of the optimal photosynthetic environment for an alpine meadow ecosystem T. Zhang et al. https://doi.org/10.1016/j.agrformet.2023.109651
18. Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products R. Yang et al. https://doi.org/10.5194/esd-13-833-2022
19. Water fluxes and energy partitioning over a Larix principis-rupprechtii plantation forest in a dryland mountain ecosystem, Northwest China C. Han et al. https://doi.org/10.1016/j.foreco.2024.122178
20. Gap‐Filled Multivariate Observations of Global Land–Climate Interactions V. Bessenbacher et al. https://doi.org/10.1029/2023JD039099
21. Highly sampled measurements in a controlled atmosphere at the Biosphere 2 Landscape Evolution Observatory J. Arevalo et al. https://doi.org/10.1038/s41597-020-00645-5
22. Estimating transpiration globally by integrating the Priestley-Taylor model with neural networks M. Hannemann et al. https://doi.org/10.1088/1748-9326/ad8506
23. Evaluation of the daily sea surface net radiation from nine satellite, reanalysis and reconstructed products and uncertainty estimates B. Jiang et al. https://doi.org/10.1016/j.srs.2025.100338
24. Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: Current status and challenges F. Jonard et al. https://doi.org/10.1016/j.agrformet.2020.108088
25. FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites J. Lai et al. https://doi.org/10.5194/gmd-18-5115-2025
26. Robust Amazon precipitation projections in climate models that capture realistic land–atmosphere interactions J. Baker et al. https://doi.org/10.1088/1748-9326/abfb2e
27. Robust historical evapotranspiration trends across climate regimes S. Hobeichi et al. https://doi.org/10.5194/hess-25-3855-2021
28. Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits J. Pacheco-Labrador et al. https://doi.org/10.1016/j.rse.2019.111362
29. Agricultural fertilization significantly enhances amplitude of land-atmosphere CO2 exchange D. Lombardozzi et al. https://doi.org/10.1038/s41467-025-56730-z
30. Multiple sources of uncertainties in satellite retrieval of terrestrial actual evapotranspiration M. Cao et al. https://doi.org/10.1016/j.jhydrol.2021.126642
31. Hybrid Machine Learning and Geostatistical Methods for Gap Filling and Predicting Solar-Induced Fluorescence Values J. Tadić et al. https://doi.org/10.3390/rs16101707
32. Constraining global terrestrial gross primary productivity in a global carbon assimilation system with OCO-2 chlorophyll fluorescence data J. Wang et al. https://doi.org/10.1016/j.agrformet.2021.108424
33. Estimating evapotranspiration using remotely sensed solar-induced fluorescence measurements K. Zhou et al. https://doi.org/10.1016/j.agrformet.2021.108800
34. Insights into evapotranspiration partitioning based on hydrological observations using the generalized proportionality hypothesis A. Hassan et al. https://doi.org/10.5194/hess-30-317-2026
35. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years J. Xiao et al. https://doi.org/10.1016/j.rse.2019.111383
36. CLIMFILL v0.9: a framework for intelligently gap filling Earth observations V. Bessenbacher et al. https://doi.org/10.5194/gmd-15-4569-2022
37. Diagnosing the representation of surface and layered soil moisture in Earth system models E. Massoud et al. https://doi.org/10.1088/1748-9326/ae2f72
38. Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods H. Feng et al. https://doi.org/10.3390/rs13040804
39. Improving the Estimation of Gross Primary Productivity across Global Biomes by Modeling Light Use Efficiency through Machine Learning D. Kong et al. https://doi.org/10.3390/rs15082086
40. Retrieval of Surface Energy Fluxes Considering Vegetation Changes and Aerosol Effects L. Chen et al. https://doi.org/10.3390/rs16040668
41. Global satellite-driven estimates of heterotrophic respiration A. Konings et al. https://doi.org/10.5194/bg-16-2269-2019
42. Solar-induced chlorophyll fluorescence sheds light on global evapotranspiration Q. Zhang et al. https://doi.org/10.1016/j.rse.2024.114061
43. Global Coordination in Plant Physiological and Rooting Strategies in Response to Water Stress Y. Liu et al. https://doi.org/10.1029/2020GB006758
44. Altimetry‐Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back‐Propagation Neural Network X. Wan et al. https://doi.org/10.1029/2022JB025785
45. Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model G. Senay et al. https://doi.org/10.1016/j.rse.2022.113011
46. From site to region: Performance evaluation of remote sensing-derived GPP products across China Y. Cao et al. https://doi.org/10.1016/j.asr.2026.01.038
47. What is global photosynthesis? History, uncertainties and opportunities Y. Ryu et al. https://doi.org/10.1016/j.rse.2019.01.016
48. Regulatory mechanisms of spatiotemporal variations in aboveground and belowground net primary production in global terrestrial ecosystems T. Zhou et al. https://doi.org/10.1016/j.agrformet.2026.111055
49. Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: Constraining an optimal stomatal model within a two-source energy balance model J. Bu et al. https://doi.org/10.1016/j.rse.2024.113999
50. Global Gross Primary Production Data Fusion by Triple Collocation Analysis From 1992 to 2018 Y. Zeng et al. https://doi.org/10.1109/JSTARS.2026.3707237
51. Using solar-induced chlorophyll fluorescence to predict winter wheat actual evapotranspiration through machine learning and deep learning methods Y. Li et al. https://doi.org/10.1016/j.agwat.2025.109322
52. Benchmarking soil moisture and its relationship to ecohydrologic variables in Earth System Models E. Massoud et al. https://doi.org/10.5194/gmd-19-3427-2026
53. Integration of flux footprint and physical mechanism into convolutional neural network model for enhanced simulation of urban evapotranspiration H. Chen et al. https://doi.org/10.1016/j.jhydrol.2022.129016
54. Triple collocation validates CONUS-wide evapotranspiration inferred from atmospheric conditions E. McCormick et al. https://doi.org/10.5194/hess-30-2417-2026
55. Impacts of benchmarking choices on inferred model skill of the Arctic–Boreal terrestrial carbon cycle J. Poe et al. https://doi.org/10.1088/2752-664X/adacee
56. Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data J. Bonsoms & G. Boulet https://doi.org/10.3390/rs14081788
57. Multiscale analysis of existing actual evapotranspiration products over agropastoral Sahel J. Etchanchu et al. https://doi.org/10.1016/j.jhydrol.2024.132585
58. Estimation of Global Cropland Gross Primary Production from Satellite Observations by Integrating Water Availability Variable in Light-Use-Efficiency Model D. Du et al. https://doi.org/10.3390/rs14071722
59. Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data J. Mosre & F. Suárez https://doi.org/10.3390/w13060870
60. Specific Responses to Environmental Factors Cause Discrepancy in the Link Between Solar-Induced Chlorophyll Fluorescence and Transpiration in Three Plantations M. Hu et al. https://doi.org/10.3390/rs17091625
61. Remote Sensing of Global Daily Evapotranspiration based on a Surface Energy Balance Method and Reanalysis Data X. Chen et al. https://doi.org/10.1029/2020JD032873
62. Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0) E. Weng et al. https://doi.org/10.5194/gmd-15-8153-2022
63. Potential of solar-induced chlorophyll fluorescence to estimate transpiration in a temperate forest X. Lu et al. https://doi.org/10.1016/j.agrformet.2018.01.017
64. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. https://doi.org/10.1038/s43017-023-00450-9
65. Advances in the Remote Sensing of Terrestrial Evaporation M. McCabe et al. https://doi.org/10.3390/rs11091138
66. Postdrought Recovery Time Across Global Terrestrial Ecosystems S. Zhang et al. https://doi.org/10.1029/2020JG005699
67. Machine Learning for Satellite Solar-Induced Fluorescence: Retrieval, Reconstruction, Downscaling, and Applications J. Verrelst et al. https://doi.org/10.3390/rs18040553
68. Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review G. Balsamo et al. https://doi.org/10.3390/rs10122038
69. Physics‐Constrained Machine Learning of Evapotranspiration W. Zhao et al. https://doi.org/10.1029/2019GL085291
70. VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing B. Wild et al. https://doi.org/10.5194/essd-14-1063-2022
71. Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution Z. Kang et al. https://doi.org/10.1007/s00376-022-1426-6
72. Could Machine Learning Break the Convection Parameterization Deadlock? P. Gentine et al. https://doi.org/10.1029/2018GL078202
73. Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe H. Wang et al. https://doi.org/10.3389/fenvs.2023.1079520
74. Interannual and Seasonal Drivers of Carbon Cycle Variability Represented by the Community Earth System Model (CESM2) W. Wieder et al. https://doi.org/10.1029/2021GB007034
75. Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors H. Zhang et al. https://doi.org/10.3390/rs15030698
76. LIDA: A Land Integrated Data Assimilation Framework for Mapping Land Surface Heat and Evaporative Fluxes by Assimilating Space‐Borne Soil Moisture and Land Surface Temperature A. Abdolghafoorian & L. Farhadi https://doi.org/10.1029/2020WR027183
77. Can we trust remote sensing evapotranspiration products over Africa? I. Weerasinghe et al. https://doi.org/10.5194/hess-24-1565-2020
78. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg https://doi.org/10.5194/gmd-16-6433-2023
79. A Review of Machine Learning Applications in Land Surface Modeling S. Pal & P. Sharma https://doi.org/10.3390/earth2010011
80. Spatiotemporal variations of forest ecohydrological characteristics in the Lancang-Mekong region during 1992-2016 and 2020-2099 under different climate scenarios A. Kayiranga et al. https://doi.org/10.1016/j.agrformet.2021.108662
81. Correcting water-stress-related bias in gross primary productivity estimates from a two-leaf light-use-efficiency model across global biomes Y. Chang et al. https://doi.org/10.1016/j.srs.2026.100476
82. Accounting for Training Data Error in Machine Learning Applied to Earth Observations A. Elmes et al. https://doi.org/10.3390/rs12061034
83. Observed water and light limitation across global ecosystems F. Jonard et al. https://doi.org/10.5194/bg-19-5575-2022
84. The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty D. Lawrence et al. https://doi.org/10.1029/2018MS001583
85. NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment M. Jasinski et al. https://doi.org/10.1175/JHM-D-17-0234.1
86. Accounting Information Disclosure and Financial Crisis Beforehand Warning Based on the Artificial Neural Network Y. Chen et al. https://doi.org/10.1155/2022/1861584
87. Coupling between the terrestrial carbon and water cycles—a review P. Gentine et al. https://doi.org/10.1088/1748-9326/ab22d6
88. A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020 W. Bi et al. https://doi.org/10.1038/s41597-022-01309-2
89. Assessing the Reliability of Global Carbon Flux Dataset Compared to Existing Datasets and Their Spatiotemporal Characteristics Z. Xiong et al. https://doi.org/10.3390/cli11100205
90. Global observed and modelled impacts of irrigation on surface temperature L. Chen & P. Dirmeyer https://doi.org/10.1002/joc.5973
91. Land–Atmosphere Interactions: The LoCo Perspective J. Santanello et al. https://doi.org/10.1175/BAMS-D-17-0001.1
92. Using solar-induced chlorophyll fluorescence to estimate forest evapotranspiration X. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.133595
93. An interpretable coupled model (SWAT-STFT) for multispatial-multistep evapotranspiration prediction in the river basin Z. Guo et al. https://doi.org/10.1016/j.agwat.2025.109742
94. Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities P. Stoy et al. https://doi.org/10.5194/bg-16-3747-2019
95. Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning J. Lu et al. https://doi.org/10.3390/f14061201
96. Data-driven estimates of evapotranspiration and its controls in the Congo Basin M. Burnett et al. https://doi.org/10.5194/hess-24-4189-2020
97. An improved spatially downscaled solar-induced chlorophyll fluorescence dataset from the TROPOMI product S. Chen et al. https://doi.org/10.1038/s41597-024-04325-6
98. Intercomparison of global terrestrial carbon fluxes estimated by MODIS and Earth system models Q. Hu et al. https://doi.org/10.1016/j.scitotenv.2021.152231
99. Estimates of Carbon Dioxide Flux into the Forest Ecosystem Based on Results of Ground-Based Hyperspectral Sounding of the Atmosphere and an Artificial Neural Network Model A. Rozanov et al. https://doi.org/10.1134/S1024856024700246
100. Seasonal patterns of canopy photosynthesis captured by remotely sensed sun-induced fluorescence and vegetation indexes in mid-to-high latitude forests: A cross-platform comparison X. Lu et al. https://doi.org/10.1016/j.scitotenv.2018.06.269
101. Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning S. Kim et al. https://doi.org/10.1038/s41597-024-03959-w
102. Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate S. Hobeichi et al. https://doi.org/10.5194/hess-22-1317-2018
103. Recent increase in the observation-derived land evapotranspiration due to global warming R. Wang et al. https://doi.org/10.1088/1748-9326/ac4291
104. Plant gross primary production, plant respiration and carbonyl sulfide emissions over the globe inferred by atmospheric inverse modelling M. Remaud et al. https://doi.org/10.5194/acp-22-2525-2022
105. Cloud‐Surface Coupling Alters the Morning Transition From Stable to Unstable Boundary Layer T. Su et al. https://doi.org/10.1029/2022GL102256
106. Machine learning in photosynthesis: Prospects on sustainable crop development R. Varghese et al. https://doi.org/10.1016/j.plantsci.2023.111795
107. A carbon sink-driven approach to estimate gross primary production from microwave satellite observations I. Teubner et al. https://doi.org/10.1016/j.rse.2019.04.022
108. Spatio‐Temporal Convergence of Maximum Daily Light‐Use Efficiency Based on Radiation Absorption by Canopy Chlorophyll Y. Zhang et al. https://doi.org/10.1029/2017GL076354
109. Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask? S. Gallup et al. https://doi.org/10.1029/2021MS002555
110. Rapid Evaluation Framework for the CMIP7 Assessment Fast Track F. Hoffman et al. https://doi.org/10.5194/gmd-19-7415-2026
111. Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data J. Zheng et al. https://doi.org/10.3390/rs17122064
112. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks Y. Zhang et al. https://doi.org/10.5194/bg-15-5779-2018
113. Understanding the diurnal cycle of land–atmosphere interactions from flux site observations E. Seo & P. Dirmeyer https://doi.org/10.5194/hess-26-5411-2022
114. AmeriFlux: Its Impact on our understanding of the ‘breathing of the biosphere’, after 25 years D. Baldocchi et al. https://doi.org/10.1016/j.agrformet.2024.109929
115. Upscaling of Latent Heat Flux in Heihe River Basin Based on Transfer Learning Model J. Lin et al. https://doi.org/10.3390/rs15071901
116. Understanding Evapotranspiration Driving Mechanisms in China With Explainable Machine Learning Algorithms H. Zhang et al. https://doi.org/10.1002/joc.8774
117. Gross primary productivity of terrestrial ecosystems: a review of observations, remote sensing, and modelling studies over South Asia V. Pandey et al. https://doi.org/10.1007/s00704-024-05158-4
118. Global downscaling of remotely sensed soil moisture using neural networks S. Alemohammad et al. https://doi.org/10.5194/hess-22-5341-2018
119. Development of a filter-based near-surface remote sensing system to retrieve far-red sun-induced chlorophyll fluorescence J. Kim et al. https://doi.org/10.1016/j.rse.2022.113311
120. Canopy Solar-Induced Chlorophyll Fluorescence and Its Link to Transpiration in a Temperate Evergreen Needleleaf Forest during the Fall Transition W. Cong et al. https://doi.org/10.3390/f13010074
121. Estimating surface soil moisture from SMAP observations using a Neural Network technique J. Kolassa et al. https://doi.org/10.1016/j.rse.2017.10.045
122. Assessment of MERRA-2 Land Surface Energy Flux Estimates C. Draper et al. https://doi.org/10.1175/JCLI-D-17-0121.1
123. Sustained Nonphotochemical Quenching Shapes the Seasonal Pattern of Solar‐Induced Fluorescence at a High‐Elevation Evergreen Forest B. Raczka et al. https://doi.org/10.1029/2018JG004883
124. Peak growing season patterns and climate extremes-driven responses of gross primary production estimated by satellite and process based models over North America W. He et al. https://doi.org/10.1016/j.agrformet.2020.108292
125. Spatiotemporal Evolution and Obstacle Factor Analysis of Land Ecological Security in the Surrounding Areas of Beijing, China Y. Wang & J. Yang https://doi.org/10.3390/land14030457