96 citations as recorded by crossref.

1. Evaluation of remotely sensed global evapotranspiration data from inland river basins Z. Liu 10.1002/hyp.14774
2. Soil-Permittivity Estimation Under Grassland Using Machine-Learning and Polarimetric Decomposition Techniques R. Hansch et al. 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. 10.1016/j.agrformet.2023.109359
4. Land–atmosphere interactions in the tropics – a review P. Gentine et al. 10.5194/hess-23-4171-2019
5. Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress G. Mohammed et al. 10.1016/j.rse.2019.04.030
6. Physics‐Constrained Machine Learning of Evapotranspiration W. Zhao et al. 10.1029/2019GL085291
7. VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing B. Wild et al. 10.5194/essd-14-1063-2022
8. 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 10.1002/2017GL076294
9. Partitioning of Historical Precipitation Into Evaporation and Runoff Based on Hydrologic Dynamics Identified With Recent SMAP Satellite Measurements R. Akbar et al. 10.1029/2020WR027307
10. Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution Z. Kang et al. 10.1007/s00376-022-1426-6
11. Could Machine Learning Break the Convection Parameterization Deadlock? P. Gentine et al. 10.1029/2018GL078202
12. Artificial Neural Network Model of Soil Heat Flux over Multiple Land Covers in South America B. de Andrade et al. 10.3390/rs13122337
13. A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning W. Shangguan et al. 10.3390/f14050913
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. 10.1016/j.agrformet.2020.108308
15. Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe H. Wang et al. 10.3389/fenvs.2023.1079520
16. Interannual and Seasonal Drivers of Carbon Cycle Variability Represented by the Community Earth System Model (CESM2) W. Wieder et al. 10.1029/2021GB007034
17. Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors H. Zhang et al. 10.3390/rs15030698
18. 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 10.1029/2020WR027183
19. A new global time-series GPP production: DFRF-GPP Z. Xiufang et al. 10.1016/j.ecolind.2024.111551
20. Analysis of the optimal photosynthetic environment for an alpine meadow ecosystem T. Zhang et al. 10.1016/j.agrformet.2023.109651
21. Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products R. Yang et al. 10.5194/esd-13-833-2022
22. Can we trust remote sensing evapotranspiration products over Africa? I. Weerasinghe et al. 10.5194/hess-24-1565-2020
23. Gap‐Filled Multivariate Observations of Global Land–Climate Interactions V. Bessenbacher et al. 10.1029/2023JD039099
24. Highly sampled measurements in a controlled atmosphere at the Biosphere 2 Landscape Evolution Observatory J. Arevalo et al. 10.1038/s41597-020-00645-5
25. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg 10.5194/gmd-16-6433-2023
26. Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: Current status and challenges F. Jonard et al. 10.1016/j.agrformet.2020.108088
27. A Review of Machine Learning Applications in Land Surface Modeling S. Pal & P. Sharma 10.3390/earth2010011
28. 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. 10.1016/j.agrformet.2021.108662
29. Accounting for Training Data Error in Machine Learning Applied to Earth Observations A. Elmes et al. 10.3390/rs12061034
30. Observed water and light limitation across global ecosystems F. Jonard et al. 10.5194/bg-19-5575-2022
31. The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty D. Lawrence et al. 10.1029/2018MS001583
32. Robust Amazon precipitation projections in climate models that capture realistic land–atmosphere interactions J. Baker et al. 10.1088/1748-9326/abfb2e
33. Robust historical evapotranspiration trends across climate regimes S. Hobeichi et al. 10.5194/hess-25-3855-2021
34. Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits J. Pacheco-Labrador et al. 10.1016/j.rse.2019.111362
35. NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment M. Jasinski et al. 10.1175/JHM-D-17-0234.1
36. Multiple sources of uncertainties in satellite retrieval of terrestrial actual evapotranspiration M. Cao et al. 10.1016/j.jhydrol.2021.126642
37. Hybrid Machine Learning and Geostatistical Methods for Gap Filling and Predicting Solar-Induced Fluorescence Values J. Tadić et al. 10.3390/rs16101707
38. Accounting Information Disclosure and Financial Crisis Beforehand Warning Based on the Artificial Neural Network Y. Chen et al. 10.1155/2022/1861584
39. Coupling between the terrestrial carbon and water cycles—a review P. Gentine et al. 10.1088/1748-9326/ab22d6
40. A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020 W. Bi et al. 10.1038/s41597-022-01309-2
41. Assessing the Reliability of Global Carbon Flux Dataset Compared to Existing Datasets and Their Spatiotemporal Characteristics Z. Xiong et al. 10.3390/cli11100205
42. Constraining global terrestrial gross primary productivity in a global carbon assimilation system with OCO-2 chlorophyll fluorescence data J. Wang et al. 10.1016/j.agrformet.2021.108424
43. Estimating evapotranspiration using remotely sensed solar-induced fluorescence measurements K. Zhou et al. 10.1016/j.agrformet.2021.108800
44. Global observed and modelled impacts of irrigation on surface temperature L. Chen & P. Dirmeyer 10.1002/joc.5973
45. Land–Atmosphere Interactions: The LoCo Perspective J. Santanello et al. 10.1175/BAMS-D-17-0001.1
46. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years J. Xiao et al. 10.1016/j.rse.2019.111383
47. CLIMFILL v0.9: a framework for intelligently gap filling Earth observations V. Bessenbacher et al. 10.5194/gmd-15-4569-2022
48. Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods H. Feng et al. 10.3390/rs13040804
49. Improving the Estimation of Gross Primary Productivity across Global Biomes by Modeling Light Use Efficiency through Machine Learning D. Kong et al. 10.3390/rs15082086
50. Retrieval of Surface Energy Fluxes Considering Vegetation Changes and Aerosol Effects L. Chen et al. 10.3390/rs16040668
51. Global satellite-driven estimates of heterotrophic respiration A. Konings et al. 10.5194/bg-16-2269-2019
52. Solar-induced chlorophyll fluorescence sheds light on global evapotranspiration Q. Zhang et al. 10.1016/j.rse.2024.114061
53. Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities P. Stoy et al. 10.5194/bg-16-3747-2019
54. Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning J. Lu et al. 10.3390/f14061201
55. Global Coordination in Plant Physiological and Rooting Strategies in Response to Water Stress Y. Liu et al. 10.1029/2020GB006758
56. Altimetry‐Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back‐Propagation Neural Network X. Wan et al. 10.1029/2022JB025785
57. Data-driven estimates of evapotranspiration and its controls in the Congo Basin M. Burnett et al. 10.5194/hess-24-4189-2020
58. Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model G. Senay et al. 10.1016/j.rse.2022.113011
59. What is global photosynthesis? History, uncertainties and opportunities Y. Ryu et al. 10.1016/j.rse.2019.01.016
60. Intercomparison of global terrestrial carbon fluxes estimated by MODIS and Earth system models Q. Hu et al. 10.1016/j.scitotenv.2021.152231
61. 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. 10.1016/j.scitotenv.2018.06.269
62. 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. 10.1016/j.rse.2024.113999
63. Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate S. Hobeichi et al. 10.5194/hess-22-1317-2018
64. Recent increase in the observation-derived land evapotranspiration due to global warming R. Wang et al. 10.1088/1748-9326/ac4291
65. Plant gross primary production, plant respiration and carbonyl sulfide emissions over the globe inferred by atmospheric inverse modelling M. Remaud et al. 10.5194/acp-22-2525-2022
66. Cloud‐Surface Coupling Alters the Morning Transition From Stable to Unstable Boundary Layer T. Su et al. 10.1029/2022GL102256
67. Integration of flux footprint and physical mechanism into convolutional neural network model for enhanced simulation of urban evapotranspiration H. Chen et al. 10.1016/j.jhydrol.2022.129016
68. Machine learning in photosynthesis: Prospects on sustainable crop development R. Varghese et al. 10.1016/j.plantsci.2023.111795
69. Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data J. Bonsoms & G. Boulet 10.3390/rs14081788
70. A carbon sink-driven approach to estimate gross primary production from microwave satellite observations I. Teubner et al. 10.1016/j.rse.2019.04.022
71. Spatio‐Temporal Convergence of Maximum Daily Light‐Use Efficiency Based on Radiation Absorption by Canopy Chlorophyll Y. Zhang et al. 10.1029/2017GL076354
72. Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask? S. Gallup et al. 10.1029/2021MS002555
73. Estimation of Global Cropland Gross Primary Production from Satellite Observations by Integrating Water Availability Variable in Light-Use-Efficiency Model D. Du et al. 10.3390/rs14071722
74. 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
75. Remote Sensing of Global Daily Evapotranspiration based on a Surface Energy Balance Method and Reanalysis Data X. Chen et al. 10.1029/2020JD032873
76. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks Y. Zhang et al. 10.5194/bg-15-5779-2018
77. 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. 10.5194/gmd-15-8153-2022
78. Understanding the diurnal cycle of land–atmosphere interactions from flux site observations E. Seo & P. Dirmeyer 10.5194/hess-26-5411-2022
79. AmeriFlux: Its Impact on our understanding of the ‘breathing of the biosphere’, after 25 years D. Baldocchi et al. 10.1016/j.agrformet.2024.109929
80. Upscaling of Latent Heat Flux in Heihe River Basin Based on Transfer Learning Model J. Lin et al. 10.3390/rs15071901
81. Global downscaling of remotely sensed soil moisture using neural networks S. Alemohammad et al. 10.5194/hess-22-5341-2018
82. Development of a filter-based near-surface remote sensing system to retrieve far-red sun-induced chlorophyll fluorescence J. Kim et al. 10.1016/j.rse.2022.113311
83. Potential of solar-induced chlorophyll fluorescence to estimate transpiration in a temperate forest X. Lu et al. 10.1016/j.agrformet.2018.01.017
84. Canopy Solar-Induced Chlorophyll Fluorescence and Its Link to Transpiration in a Temperate Evergreen Needleleaf Forest during the Fall Transition W. Cong et al. 10.3390/f13010074
85. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
86. Estimating surface soil moisture from SMAP observations using a Neural Network technique J. Kolassa et al. 10.1016/j.rse.2017.10.045
87. Advances in the Remote Sensing of Terrestrial Evaporation M. McCabe et al. 10.3390/rs11091138
88. Assessment of MERRA-2 Land Surface Energy Flux Estimates C. Draper et al. 10.1175/JCLI-D-17-0121.1
89. Sustained Nonphotochemical Quenching Shapes the Seasonal Pattern of Solar‐Induced Fluorescence at a High‐Elevation Evergreen Forest B. Raczka et al. 10.1029/2018JG004883
90. 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. 10.1016/j.agrformet.2020.108292
91. Postdrought Recovery Time Across Global Terrestrial Ecosystems S. Zhang et al. 10.1029/2020JG005699
92. Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review G. Balsamo et al. 10.3390/rs10122038
93. A water risk index for portfolio exposure to climatic extremes: conceptualization and an application to the mining industry L. Bonnafous et al. 10.5194/hess-21-2075-2017
94. Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates B. Pagán et al. 10.3390/rs11040413
95. Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets R. Wartenburger et al. 10.1088/1748-9326/aac4bb
96. The future of Earth observation in hydrology M. McCabe et al. 10.5194/hess-21-3879-2017