FLUXCOM-X daily gross primary productivity on global 0.25 degree grid for 2005

X-BASE GPP 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. The GPP estimates from the eddy covariance data was based on the Nighttime Partitioning method.

Published paper: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-165/
Nelson, J.A., Walther, S., Jung, M., Gans, F., Kraft, B., Weber, U., Hamdi, Z., Duveiller, G., Zhang, W., 2023. FLUXCOM-X-BASE. https://doi.org/10.18160/5NZG-JMJE

X-BASE GPP 构建于 FLUXCOM-X 框架之上，该框架通过对原位涡度协方差数据进行机器学习模型的训练，并利用这些模型生成全球产品。X-BASE 实验构成 FLUXCOM-X 框架的基本配置，旨在作为基准，其中包含核心气象数据、植被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。在本次研究中，XGBoost 被用作机器学习算法。基于涡度协方差数据的 GPP 估算采用夜间分割方法进行。

相关论文：https://egusphere.copernicus.org/preprints/2024/egusphere-2024-165/

作者：Nelson, J.A., Walther, S., Jung, M., Gans, F., Kraft, B., Weber, U., Hamdi, Z., Duveiller, G., Zhang, W., 2023. FLUXCOM-X-BASE. https://doi.org/10.18160/5NZG-JMJE