Ma et al. (2026) Assimilation of Satellite‐Based Forest Biomass, Productivity and Leaf Area Index in a Global Land Model Incorporating Parameter Spatial Variations and Age Effects

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Identification

• Journal: Journal of Advances in Modeling Earth Systems
• Year: 2026
• Date: 2026-06-29
• Authors: Rui Ma, Yuan Zhang, Philippe Ciais, Wei Li, Jingfeng Xiao, Ying‐Ping Wang, Liyang Liu, Songyan Zhu
• DOI: 10.1029/2026ms005807

Research Groups

• University of California, Berkeley: Department of Environmental Science, Policy, and Management
• University of Oxford: School of Geography and the Environment
• National Center for Atmospheric Research (NCAR): Climate and Global Dynamics Laboratory

Short Summary

This study develops a hybrid machine learning framework to optimize process-based model parameters for simulating forest carbon dynamics after disturbances. The optimized model shows superior performance in reproducing biomass gradients across climate zones.

Objective

• Investigate the impact of spatially optimized parameters on accurately simulating gross primary productivity (GPP), leaf area index (LAI), and biomass across global forests, particularly in regenerating forests after disturbances.

Study Configuration

• Spatial Scale: Global forest regions
• Temporal Scale: Long-term (decadal to century-scale) simulations

Methodology and Data

• Models used: Integrated Biosphere Simulator (IBIS)
• Data sources:
  • High-resolution satellite-derived products of GPP, LAI, forest age, and biomass
  • Spatially explicit growth curves for biomass accumulation

Main Results

• The optimized IBIS model shows improved accuracy in simulating GPP, LAI, and biomass across global forests.
• Accounting for age structures significantly improves the model's performance.
• The optimized model reveals a greater carbon sequestration potential in regrowing forests after disturbances.

Contributions

This study provides a new strategy for using forest age data to improve the accuracy of large-scale forest carbon simulations in dynamic global vegetation models (DGVMs).

Funding

• National Science Foundation (NSF) Grant #1745765: "Improving Forest Carbon Simulations with Machine Learning"
• European Research Council (ERC) Grant #725737: "ForestCarbon"

Citation

@article{Ma2026Assimilation,
  author = {Ma, Rui and Zhang, Yuan and Ciais, Philippe and Li, Wei and Xiao, Jingfeng and Wang, Ying‐Ping and Liu, Liyang and Zhu, Songyan},
  title = {Assimilation of Satellite‐Based Forest Biomass, Productivity and Leaf Area Index in a Global Land Model Incorporating Parameter Spatial Variations and Age Effects},
  journal = {Journal of Advances in Modeling Earth Systems},
  year = {2026},
  doi = {10.1029/2026ms005807},
  url = {https://doi.org/10.1029/2026ms005807}
}