UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2018

UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2018 OverviewThe 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 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# 2018年UFLUX集成欧洲100米半年尺度数据集（European 100 6-monthly） ## 概述 UFLUX集成数据集（UFLUX Ensemble Dataset）采用深森林机器学习模型（Deep Forest machine learning models）构建，提供空间分辨率为100米的欧洲陆地通量数据。该数据集融合了基于卫星的Sentinel-2植被代理变量近红外植被指数（NIRv）与ERA5气候再分析资料（ERA5 climate reanalysis），并以ICOS涡度协方差观测（ICOS eddy covariance observations）作为训练基准。UFLUX项目包含5类核心通量组分： 1. 总初级生产力（Gross Primary Production, GPP） 2. 生态系统呼吸（Ecosystem Respiration, RECO） 3. 净生态系统交换（Net Ecosystem Exchange, NEE） 4. 感热通量（Sensible Heat Flux, H） 5. 潜热通量（Latent Energy Flux, LE） ## 背景与方法 统一通量（Unified FLUXes, UFLUX）计划是一个数据驱动、基于机器学习的平台，旨在将塔站观测的涡度协方差（eddy covariance, EC）通量测量结果尺度上推至全球范围，以期解答气候变化背景下陆地生态系统管理效能相关的关键科学问题。 UFLUX的核心创新点包括： 1. **统一尺度上推框架**：采用基于深度决策树的方法，协调不同时空尺度与多种通量类型（如GPP、RECO等）的通量尺度上推流程，相比传统神经网络更适配EC通量数据特性。 2. **混合可解释机器学习模型**：通过残差学习（residual learning）将黑箱机器学习模型与生态学可解释性相结合，在保障预测能力的同时，为科研人员提供新的科学认知（UFLUXv2版本）。 3. **不确定性量化**：采用采样空间完备性方法，以透明、稳健的方式评估模型不确定性。 4. **多源数据融合**：充分利用植被代理变量（如NIRv、日光诱导叶绿素荧光SIF）与气候数据（如ERA5）的互补优势，相比单源数据方法更全面地表征碳动态过程。 5. **优异的间隙填充性能**：UFLUX最初作为全球EC通量间隙填充工具开发，相较于传统方法，其精度提升可达30%，不确定性降低幅度最高达70%。 6. **高性能表现**：在全球尺度上实现了优异的预测精度，其中RECO的全局决定系数R²＞0.8，GPP的R²≈0.9；同时计算效率极高，可在标准笔记本电脑上运行。 7. **社区广泛采用**：目前已被多个全球尺度上推项目使用，印证了其可靠性与影响力。 ## 应用场景 UFLUX适用于研究陆地管理、气候变化与碳通量之间的相互作用，尤其可通过修正EC测量偏差，优化全球GPP与RECO的估算精度。 ## 相关资源 - UFLUX官方网站：https://sites.google.com/view/uflux - 代码仓库：https://github.com/soonyenju/uflux - 技术与描述性论文：https://doi.org/10.1080/01431161.2024.2312266