UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2017

UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2017

Overview
The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using Deep Forest machine learning models. It integrates satellite-based Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. The UFLUX project includes five core flux components:

• Gross Primary Production (GPP)

• Ecosystem Respiration (RECO)

• Net Ecosystem Exchange (NEE)

• Sensible Heat Flux (H)

• Latent Energy Flux (LE)

Background and Methodology
The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:

1. Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.

2. Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).

3. Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.

4. Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.

5. Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.

6. High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.

7. Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.

Applications
UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources

• UFLUX Website: https://sites.google.com/view/uflux

• Code Repository: https://github.com/soonyenju/uflux

• Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266