FLUXCOM-X monthly diurnal cycle of net ecosystem exchange on global 0.25 degree grid for 2007

X-BASE NEE (Net Ecosystem Exchange) is based on the FLUXCOM-X framework which trains machine learning models on in-situ eddy covariance data and uses them to produce this global product. The X-BASE experiment is a basic configuration to serve as a baseline for the FLUXCOM-X framework and includes as predictors the core meteorlogical data, plant functional type classification as well as MODIS based vegitation indicies and land surface temperature. XGBoost was used as the machine learning algorithm.
Gans, F., Duveiller, G., Hamdi, Z., Jung, M., Kraft, B., Nelson, J., Walther, S., Weber, U., Zhang, W. (2023). FLUXCOM-X monthly diurnal cycle of net ecosystem exchange on global 0.25 degree grid for 2007, Miscellaneous, https://hdl.handle.net/11676/0zP9AJBZKBebTao9XC7YJfuK

X-BASE NEE（网络生态系统交换）基于FLUXCOM-X框架，该框架通过现场涡度协方差数据训练机器学习模型，并利用这些模型生成全球产品。X-BASE实验作为一种基本配置，旨在为FLUXCOM-X框架提供一个基准，其中包含核心气象数据、植物功能型分类以及基于MODIS的植被指数和地表温度作为预测因子。该实验采用了XGBoost作为机器学习算法。
Gans, F., Duveiller, G., Hamdi, Z., Jung, M., Kraft, B., Nelson, J., Walther, S., Weber, U., Zhang, W. (2023). FLUXCOM-X全球0.25度网格上2007年的月日变化净生态系统交换，杂项，https://hdl.handle.net/11676/0zP9AJBZKBebTao9XC7YJfuK

搜集汇总

背景与挑战

背景概述

该数据集提供了2007年全球0.25度网格上的月度日循环净生态系统交换（NEE）数据，基于FLUXCOM-X框架和XGBoost机器学习算法生成，结合了气象、植被等多源数据。数据集时间分辨率为每小时，覆盖全球范围，适用于生物地球化学循环和生态系统功能研究。