60 citations as recorded by crossref.

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13. A Comparison of Land Surface Phenology in the Northern Hemisphere Derived from Satellite Remote Sensing and the Community Land Model X. Li et al. https://doi.org/10.1175/JHM-D-21-0169.1
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18. New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets Z. Chang et al. https://doi.org/10.3390/rs13152892
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22. Advancing Sustainable Development Goals through Earth Observation Satellite Data: Current Insights and Future Directions Q. Zhao & L. Yu https://doi.org/10.34133/remotesensing.0403
23. Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture S. Li et al. https://doi.org/10.5194/hess-26-6311-2022
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25. Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth M. Forkel et al. https://doi.org/10.5194/hess-27-39-2023
26. Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance G. Yin et al. https://doi.org/10.3390/ijgi7070242
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34. Impacts of shifting phenology on boundary layer dynamics in North America in the CESM X. Li et al. https://doi.org/10.1016/j.agrformet.2022.109286
35. Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion X. Ren et al. https://doi.org/10.1016/j.ecolmodel.2018.03.013
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42. Insights from Time Series of Atmospheric Carbon Dioxide and Related Tracers R. Keeling & H. Graven https://doi.org/10.1146/annurev-environ-012220-125406
43. 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
44. The Role of Solar-Induced Chlorophyll Fluorescence (SIF) in the Mechanistic Simulation of Eco-Hydrological Processes A. Cui et al. https://doi.org/10.3390/rs18091364
45. Modeling phenological controls on carbon dynamics in dryland sagebrush ecosystems K. Renwick et al. https://doi.org/10.1016/j.agrformet.2019.04.003
46. Advancements in remote sensing for active fire detection: A review of datasets and methods S. Yang et al. https://doi.org/10.1016/j.scitotenv.2024.173273
47. Understanding the Land Carbon Cycle with Space Data: Current Status and Prospects J. Exbrayat et al. https://doi.org/10.1007/s10712-019-09506-2
48. An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends Q. Zhao et al. https://doi.org/10.3390/rs14081863
49. What is the Uncertainty of the Uncertainty and (Why) Does it Matter? Improving the Uncertainty Estimates of Merged Multi-satellite Soil Moisture Data Sets M. Formanek et al. https://doi.org/10.1007/s10712-026-09944-9
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51. Terrestrial carbon cycle model-data fusion: Progress and challenges X. Li et al. https://doi.org/10.1007/s11430-020-9800-3
52. Enhanced predictions of measurable soil carbon pools in the Australian rangelands with the Millennial v2 model M. Zhang & R. Viscarra Rossel https://doi.org/10.1016/j.geoderma.2026.117811
53. Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET H. Dokoohaki et al. https://doi.org/10.5194/gmd-15-3233-2022
54. Simulation and Assimilation of CO2 Concentrations Based on the WRF-Chem Model W. Liu et al. https://doi.org/10.3390/pr13124010
55. Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observations K. Castro-Morales et al. https://doi.org/10.5194/bg-16-3009-2019
56. Inferring management and predicting sub-field scale C dynamics in UK grasslands using biogeochemical modelling and satellite-derived leaf area data V. Myrgiotis et al. https://doi.org/10.1016/j.agrformet.2021.108466
57. Remote sensing of plant-water relations: An overview and future perspectives A. Damm et al. https://doi.org/10.1016/j.jplph.2018.04.012
58. An exploration of solar-induced chlorophyll fluorescence (SIF) factors simulated by SCOPE for capturing GPP across vegetation types S. Yang et al. https://doi.org/10.1016/j.ecolmodel.2022.110079
59. 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
60. China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables W. He et al. https://doi.org/10.1029/2021JG006644