UFLUX global ensemble 0.25deg monthly carbon, water, and energy fluxes from 2001 - 2021

UFLUX Ensemble Globe025dmonthly (Global 0.25° Monthly, 13 Members) OverviewThe UFLUX ensemble dataset provides global monthly fluxes at 0.25° spatial resolution, incorporating 13 ensemble members derived from different combinations of satellite-based vegetation proxies and climate reanalysis data. The dataset includes five key ecosystem flux components: Gross Primary Production (GPP) Ecosystem Respiration (RECO) Net Ecosystem Exchange (NEE) Sensible Heat Flux (H) Latent Energy Flux (LE) Ensemble Members:Each member combines unique satellite vegetation indices with climate datasets: MODIS-NIRv-CFSV2 MODIS-NIRv-ERA5 OCO-2-CSIF-ERA5 GOME-2-SIF-ERA5 GOSAT-755-SIF-ERA5 GOSAT-772-SIF-ERA5 MODIS-NDVI-ERA5 MODIS-EVI2-ERA5 AVHRR-NIRv-ERA5 AVHRR-NDVI-ERA5 AVHRR-EVI2-ERA5 MODIS-NIRv-ERA5-WY MODIS-NIRv-ERA5-NT Background and MethodologyThe 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: 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. Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. 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. 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. 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. Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. ApplicationsUFLUX 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