FLUXCOM Global Land Carbon Fluxes

FLUXCOM used machine learning to merge carbon flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) and their uncertainties. The resulting database comprises more than 100 global gridded products in two setups: (1) using exclusively MODIS remote sensing data (RS) on a 0.0833° grid, and (2) using remote sensing and meteorological data (RS+METEO) at 0.5° resolution. Within each setup we use a full factorial design across multiple machine learning methods, climate forcing datasets (for RS-METEO) and two flux partitioning approaches. Here we provide these data on a monthly resolution and provide also FLUXCOM ensemble products. Please see Jung et al. 2020, BG and Tramontana et al. 2016, BG before using the data, and cite both papers in publications. These data are freely available on a CC4.0 BY license.

FLUXCOM 采用机器学习方法，将FLUXNET涡度协方差塔的碳通量观测数据与遥感及气象数据进行融合，以估算总初级生产力（gross primary productivity, GPP）、陆地生态系统呼吸（terrestrial ecosystem respiration, TER）、净生态系统交换（net ecosystem exchange, NEE）及其不确定性。所生成的数据库涵盖两种配置下的百余套全球网格化产品：（1）仅采用MODIS遥感数据（RS）的0.0833°分辨率网格化方案；（2）结合遥感与气象数据（RS+METEO）的0.5°分辨率网格化方案。每种配置方案下均采用全因子实验设计，覆盖多种机器学习方法、气候强迫数据集（仅针对RS+METEO配置）以及两种通量拆分方法。本数据集提供月度分辨率数据，同时还提供FLUXCOM集合产品。在使用本数据集前，请参阅Jung等（2020）发表于《Biogeosciences》（BG）以及Tramontana等（2016）发表于BG的研究成果，并在正式发表的成果中引用这两篇文献。本数据集采用CC BY 4.0（知识共享署名4.0）许可协议，可免费获取使用。

搜集汇总

背景与挑战

背景概述

FLUXCOM Global Land Carbon Fluxes数据集通过机器学习方法合并了FLUXNET涡度协方差塔的碳通量测量数据与遥感和气象数据，估算了全球陆地生态系统的总初级生产力(GPP)、生态系统呼吸(TER)和净生态系统交换(NEE)及其不确定性。数据集包含超过100个全球网格化产品，提供两种分辨率（0.0833°和0.5°）的月度数据，并采用CC4.0 BY许可自由使用。