alexroz/CarbonFluxBench

CarbonFluxBench: A Global Benchmark for Upscaling of Carbon Fluxes Using Zero-Shot Learning

CarbonFluxBench comprises over 1.3 million daily observations from 573 eddy covariance flux tower sites globally (2000–2024). It provides stratified evaluation protocols that explicitly test generalization across unseen vegetation types and climate regimes, a harmonized set of remote sensing and meteorological features, and reproducible baselines ranging from tree-based methods to domain-generalization architectures.

Paper: [CarbonFluxBench (KDD 2026)]() Code: github.com/alexxxroz/CarbonFluxBench

Dataset Summary

Data Files

Prediction Targets

All targets are derived from eddy covariance measurements standardized under the ONEFlux methodology (units: gC m⁻² day⁻¹):

Each observation includes a continuous quality control flag: NEE_VUT_USTAR50_QC (0–1).

Features

MODIS MOD09GA (12 features)

Seven surface reflectance bands (sur_refl_b01–sur_refl_b07), sensor/solar geometry (SensorZenith, SensorAzimuth, SolarZenith, SolarAzimuth), and cloud fraction (clouds).

ERA5-Land (6 / 36 / 150 features)

Three configurable feature sets defined in feature_sets.json:

• —Minimal (6): temperature, precipitation, radiation, evaporation, LAI (high & low vegetation)
• —Standard (36): minimal + soil temperature/moisture (4 levels), wind, pressure, snow/albedo, radiation components, runoff
• —Full (150): standard + lake variables, additional flux components, min/max daily variants

Site Metadata (5 features)

Latitude, longitude, IGBP vegetation type (16 classes), Köppen climate class (5 main / 30 detailed).

Train-Test Splits

CarbonFluxBench provides two complementary site-holdout splits for zero-shot evaluation (random state = 56):

• —IGBP-stratified: partitioned by vegetation type. 80/20 for common classes (>10 sites), 50/50 for rare classes (≤10 sites).
• —Köppen-stratified: partitioned by climate zone. Uniform 80/20 split across 5 main classes.

All splits are at the site level — train and test sites are mutually exclusive.

Usage

Download

pip install huggingface_hub
huggingface-cli download alexroz/CarbonFluxBench --repo-type dataset --local-dir data

With the CarbonFluxBench package

import carbonfluxbench

targets = ['GPP_NT_VUT_USTAR50', 'RECO_NT_VUT_USTAR50', 'NEE_VUT_USTAR50']
y = carbonfluxbench.load_targets(targets, include_qc=True)
y_train, y_test = carbonfluxbench.split_targets(y, split_type='Koppen')

modis = carbonfluxbench.load_modis()
era = carbonfluxbench.load_era('minimal')
train, val, test, x_scaler, y_scaler = carbonfluxbench.join_features(
    y_train, y_test, modis, era, scale=True
)

Direct loading with pandas

import pandas as pd

targets = pd.read_parquet("data/target_fluxes.parquet")
modis = pd.read_parquet("data/MOD09GA.parquet")
era5 = pd.read_parquet("data/ERA5.parquet")

Evaluation

All metrics are computed per-site, then reported as quantiles (25th, median, 75th percentile):

Citation

<!-- ```bibtex @inproceedings{carbonfluxbench, title={CarbonFluxBench: A Global Benchmark for Upscaling of Carbon Fluxes Using Zero-Shot Learning}, author={Rozanov, Aleksei and Renganathan, Arvind and Zhang, Yimeng and Kumar, Vipin}, booktitle={Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, year={2025} }

## License

MIT