30 citations as recorded by crossref.

1. Enhancing carbon flux estimation in a crop growth model by integrating UAS-derived leaf area index X. Guo et al. https://doi.org/10.1016/j.agrformet.2025.110776
2. Integrating partitioned evapotranspiration data into hydrologic models: Vegetation parameterization and uncertainty quantification of simulated plant water use A. Schreiner‐McGraw et al. https://doi.org/10.1002/hyp.14580
3. A Two-Step Framework for Generating 0.01°, Hourly, and Gapless Land Surface Temperature J. Ma et al. https://doi.org/10.1109/JSTARS.2024.3503578
4. Incorporating dynamic crop growth processes and management practices into a terrestrial biosphere model for simulating crop production in the United States: Toward a unified modeling framework Y. You et al. https://doi.org/10.1016/j.agrformet.2022.109144
5. Dynamic parameterization of soil surface characteristics for hydrological models in agricultural catchments T. Grangeon et al. https://doi.org/10.1016/j.catena.2022.106257
6. Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2) E. Sinha et al. https://doi.org/10.1029/2022MS003171
7. Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation F. Wang et al. https://doi.org/10.1016/j.heliyon.2024.e27549
8. The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2) E. Sinha et al. https://doi.org/10.1029/2022JG007187
9. Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach T. Boas et al. https://doi.org/10.5194/hess-27-3143-2023
10. Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation J. Baumert et al. https://doi.org/10.1016/j.ecoinf.2024.102836
11. Field-scale evaluation of a satellite-based terrestrial biosphere model for estimating crop response to management practices and productivity J. Wang et al. https://doi.org/10.1016/j.isprsjprs.2024.11.008
12. Exploring the effects of extreme weather events on methane emissions from croplands: A study combining site and global modeling Y. Xia et al. https://doi.org/10.1016/j.agrformet.2023.109454
13. Improved Mechanistic Modeling of Global Vegetation-Mediated Mercury Deposition Q. Chen et al. https://doi.org/10.1021/acsestair.5c00374
14. Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands Y. Zhu et al. https://doi.org/10.3390/rs15153789
15. Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale? F. Li et al. https://doi.org/10.1029/2023WR035056
16. Effects of Extreme Weather Events on Nitrous Oxide Emissions from Rice-Wheat Rotation Croplands Y. Xia et al. https://doi.org/10.3390/plants13010025
17. Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes W. Sun et al. https://doi.org/10.1029/2020AV000310
18. Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes A. Tai et al. https://doi.org/10.5194/gmd-17-3733-2024
19. Phenology, fluxes and their drivers in major Indian agroecosystems: A modeling study using the Community Land Model (CLM5) K. Reddy & S. Baidya Roy https://doi.org/10.5194/bg-23-1365-2026
20. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review K. Berger et al. https://doi.org/10.1016/j.rse.2022.113198
21. Isolating the effects of land use land cover change and inter-decadal climate variations on the water and energy cycles over India, 1981–2010 N. Ghodichore et al. https://doi.org/10.1016/j.jhydrol.2022.128267
22. CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5) O. Dombrowski et al. https://doi.org/10.5194/gmd-15-5167-2022
23. Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication G. De Lannoy et al. https://doi.org/10.3389/frwa.2022.981745
24. Breeding changes water use of winter wheat across Europe D. Behrend et al. https://doi.org/10.1038/s44264-026-00135-y
25. Cosmic-ray neutron sensors provide scale-appropriate soil water content and vegetation observations for eddy covariance stations in agricultural ecosystems C. Brogi et al. https://doi.org/10.1016/j.agrformet.2025.110731
26. Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data L. Strebel et al. https://doi.org/10.5194/hess-28-1001-2024
27. T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation J. Buckley Paules et al. https://doi.org/10.5194/gmd-18-1287-2025
28. Modeling gross primary production and transpiration from sun-induced chlorophyll fluorescence using a mechanistic light-response approach Q. Beauclaire et al. https://doi.org/10.1016/j.rse.2024.114150
29. Point-scale multi-objective calibration of the Community Land Model (version 5.0) using in situ observations of water and energy fluxes and variables T. Denager et al. https://doi.org/10.5194/hess-27-2827-2023
30. Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data K. Reddy et al. https://doi.org/10.5194/gmd-18-763-2025