📘 About this repository

This is based on my previous paper published in Agricultural and Forest Meteorology. I have better organized the code in google colab, making it more user-friendly for everyone interested in gap-filling flux data using a machine learning model XGBoost. The work is supported by NEON ambassador program.

🚀 Get started using google colab

Google Colab

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📬 Questions or Collaborations?

If you have any questions, suggestions, or are interested in collaborating, feel free to reach out! [email protected]

📝 Citation

Liu, Yujie, et al. (2025). Robust filling of extra-long gaps in eddy covariance CO₂ flux measurements from a temperate deciduous forest using eXtreme Gradient Boosting. Agricultural and Forest Meteorology, 364, 110438. https://doi.org/10.1016/j.agrformet.2025.110438

• 🐍 Python environment: environment.yml

• 📂 Input data: data_for_XGB_BART_NEON.csv

  • PPFD, Tair, and VPD are gapfilled using MDS
  • NEE_for_gapfill is processed after IQR and u* filtering using REddyProc. You can find out how to do that from the tutorial one here: https://github.com/YujieLiu666/Bridginggap-flux
  • Processing input data using REddyProc? Tutorial can be found here!

• 📜 Script:

  • All functions are stored in function_XGB.py
  • Workflow: workflow_XGB.ipynb to run the functions

• 💾 Output:

  • Model after hyperparameter tuning: saved in subfolder /XGB_models

Binder (experimental, in progress)