48 citations as recorded by crossref.

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5. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years J. Xiao et al. 10.1016/j.rse.2019.111383
6. Validation of the Community Land Model Version 5 Over the Contiguous United States (CONUS) Using In Situ and Remote Sensing Data Sets Y. Cheng et al. 10.1029/2020JD033539
7. 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
8. 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
9. 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
10. The CO 2 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
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12. Global hunter-gatherer population densities constrained by influence of seasonality on diet composition D. Zhu et al. 10.1038/s41559-021-01548-3
13. Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration B. Chen et al. 10.3390/rs13204075
14. Advances in the Remote Sensing of Terrestrial Evaporation M. McCabe et al. 10.3390/rs11091138
15. Approaches to investigate crop responses to ozone pollution: from O 3 ‐FACE to satellite‐enabled modeling C. Montes et al. 10.1111/tpj.15501
16. 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
17. Earth system data cubes unravel global multivariate dynamics M. Mahecha et al. 10.5194/esd-11-201-2020
18. Upscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning O. Reitz et al. 10.1029/2020JG005814
19. 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
20. Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks G. Tramontana et al. 10.1111/gcb.15203
21. 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
22. Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half J. Liu et al. 10.1111/gcb.15247
23. Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability Y. Zhang et al. 10.3390/rs13071289
24. 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
25. Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests S. Besnard et al. 10.1371/journal.pone.0211510
26. 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
27. 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
28. Generating Spatially Robust Carbon Budgets From Flux Tower Observations A. Griebel et al. 10.1029/2019GL085942
29. 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
30. Radiance-based NIRv as a proxy for GPP of corn and soybean G. Wu et al. 10.1088/1748-9326/ab65cc
31. Crowd‐sourced plant occurrence data provide a reliable description of macroecological gradients M. Mahecha et al. 10.1111/ecog.05492
32. 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
33. 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
34. 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
35. Global variability in belowground autotrophic respiration in terrestrial ecosystems X. Tang et al. 10.5194/essd-11-1839-2019
36. Monitoring tropical forests under a functional perspective with satellite‐based vegetation optical depth G. Vaglio Laurin et al. 10.1111/gcb.15072
37. Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network Z. Xu et al. 10.1016/j.agrformet.2020.108010
38. Spatial and temporal patterns of global soil heterotrophic respiration in terrestrial ecosystems X. Tang et al. 10.5194/essd-12-1037-2020
39. 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
40. Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model N. Huang et al. 10.1109/JSTARS.2021.3114190
41. 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
42. A decreasing carbon allocation to belowground autotrophic respiration in global forest ecosystems X. Tang et al. 10.1016/j.scitotenv.2021.149273
43. What is global photosynthesis? History, uncertainties and opportunities Y. Ryu et al. 10.1016/j.rse.2019.01.016
44. 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
45. Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration R. Khanal et al. 10.3390/atmos12121565
46. Evaluating Different Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale T. Xu et al. 10.1029/2018JD028447
47. Quantifying aboveground vegetation water storage combining Landsat 8 OLI and Sentinel-1 imageries X. Luo et al. 10.1080/10106049.2020.1861662
48. 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