34 citations as recorded by crossref.

1. Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction D. Lu et al. https://doi.org/10.3389/fenrg.2021.752185
2. An integrated data assimilation, crop modeling, and multi-objective optimization framework for improving cotton irrigation water use efficiency Y. Liu et al. https://doi.org/10.1016/j.agwat.2025.109774
3. Sentinel-1 soil moisture content and its uncertainty over sparsely vegetated fields H. Benninga et al. https://doi.org/10.1016/j.hydroa.2020.100066
4. Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization D. Lu et al. https://doi.org/10.1002/2017MS001134
5. A dynamic mathematical model for uncertainty-aware prediction and optimization of microalgal mixotrophic cultivation in a photobioreactor A. Singathia et al. https://doi.org/10.1016/j.bej.2026.110366
6. Ensemble Numerical Experiments with Earth System Models A. Eliseev https://doi.org/10.1007/s11141-026-10472-0
7. An adaptive Kriging surrogate method for efficient joint estimation of hydraulic and biochemical parameters in reactive transport modeling J. Zhou et al. https://doi.org/10.1016/j.jconhyd.2018.08.005
8. A filter for tracking non-cooperative low-thrust satellites using surveillance radar data G. Escribano et al. https://doi.org/10.1016/j.actaastro.2023.09.026
9. Identifying Data Needed to Reduce Parameter Uncertainty in a Coupled Microbial Soil C and N Decomposition Model M. Saifuddin et al. https://doi.org/10.1029/2021JG006593
10. Automatic maneuver detection and tracking of space objects in optical survey scenarios based on stochastic hybrid systems formulation G. Escribano et al. https://doi.org/10.1016/j.asr.2022.02.034
11. Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation I. Fer et al. https://doi.org/10.5194/bg-15-5801-2018
12. Bayesian calibration of simple forest models with multiplicative mathematical structure: A case study with two Light Use Efficiency models in an alpine forest M. Bagnara et al. https://doi.org/10.1016/j.ecolmodel.2018.01.014
13. Sequential Likelihood-Free Inference with Neural Proposal D. Kim et al. https://doi.org/10.1016/j.patrec.2023.03.021
14. Beyond deterministic air quality modeling: a probabilistic screening approach for emission inputs in AERMOD Z. Emerson & T. Khan https://doi.org/10.1093/inteam/vjaf098
15. Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling A. Chen et al. https://doi.org/10.1029/2022MS003135
16. Simulating the land carbon sink: Progresses and challenges of terrestrial ecosystem models W. Yuan et al. https://doi.org/10.1016/j.agrformet.2024.110264
17. A model-independent data assimilation (MIDA) module and its applications in ecology X. Huang et al. https://doi.org/10.5194/gmd-14-5217-2021
18. Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo V. Aakula et al. https://doi.org/10.1016/j.eja.2025.127769
19. Ecosystem service tradeoff and synergy mechanisms in the Central Asian terminal lake basin based on Bayesian Networks J. Li et al. https://doi.org/10.1016/j.ecoser.2025.101768
20. A Bayesian inversion framework to evaluate parameter and predictive inference of a simple soil respiration model in a cool-temperate forest in western Japan M. Toda et al. https://doi.org/10.1016/j.ecolmodel.2019.108918
21. An improved estimate of soil carbon pool and carbon fluxes in the Qinghai-Tibetan grasslands using data assimilation with an ecosystem biogeochemical model R. Zhao et al. https://doi.org/10.1016/j.geoderma.2022.116283
22. An Integrative Model for Soil Biogeochemistry and Methane Processes: I. Model Structure and Sensitivity Analysis D. Ricciuto et al. https://doi.org/10.1029/2019JG005468
23. A deep learning-based direct forecasting of CO2 plume migration M. Fan et al. https://doi.org/10.1016/j.geoen.2022.211363
24. Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data R. Ma et al. https://doi.org/10.5194/gmd-15-6637-2022
25. Calibrating the soil organic carbon model Yasso20 with multiple datasets T. Viskari et al. https://doi.org/10.5194/gmd-15-1735-2022
26. Biophysically Informed Imaging Acquisition of Plant Water Status D. Beverly et al. https://doi.org/10.3389/ffgc.2020.589493
27. Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations S. Wang & Y. Wang https://doi.org/10.1007/s00382-019-04702-7
28. Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques D. Lu & D. Ricciuto https://doi.org/10.5194/gmd-12-1791-2019
29. Comparison of GLUE and DREAM for the estimation of cultivar parameters in the APSIM-maize model M. Sheng et al. https://doi.org/10.1016/j.agrformet.2019.107659
30. Identifying sensitive parameters of global major staple crops in the Noah-MP Crop model: Similarities and differences J. Fang et al. https://doi.org/10.1016/j.eja.2026.128289
31. A semiempirical model for horizontal distribution of surface wind speed leeward windbreaks F. Yuan et al. https://doi.org/10.1007/s10457-019-00417-0
32. Combining Eddy Covariance Towers, Field Measurements, and the MEMS 2 Ecosystem Model Improves Confidence in the Climate Impacts of Bioenergy With Carbon Capture and Storage G. Falvo et al. https://doi.org/10.1111/gcbb.70023
33. Hyperspectral surface reflectance improves GPP estimation in terrestrial biosphere modeling using model-data fusion H. Liu et al. https://doi.org/10.1016/j.rse.2025.114989
34. Considering coasts: Adapting terrestrial models to characterize coastal wetland ecosystems T. O'Meara et al. https://doi.org/10.1016/j.ecolmodel.2021.109561