FLUXCOM-X daily gross primary productivity on global 0.25 degree grid for 2020

X-BASE GPP (Gross Primary Productivity) 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. Gans, F., Duveiller, G., Hamdi, Z., Jung, M., Kraft, B., Nelson, J., Walther, S., Weber, U., Zhang, W. (2023). FLUXCOM-X daily gross primary productivity on global 0.25 degree grid for 2020, Miscellaneous, https://hdl.handle.net/11676/rxIGSHSrUaC26fww3865GNkVX-BASE GPP（总初级生产力）基于FLUXCOM-X框架，该框架通过在原位涡度协方差数据上训练机器学习模型，并利用这些模型生成全球生产力数据。X-BASE实验作为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。实验中采用了XGBoost作为机器学习算法。涡度协方差数据中的GPP估计基于夜间分割方法。 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度网格下的2020年每日总初级生产力，杂项，https://hdl.handle.net/11676/rxIGSHSrUaC26fww3865GNkV

搜集汇总

背景与挑战

背景概述

该数据集是基于FLUXCOM-X框架生成的2020年全球每日总初级生产力（GPP）产品，空间分辨率为0.25度网格。它使用XGBoost机器学习算法，通过训练原位涡度协方差数据，并整合气象数据、植物功能类型、MODIS植被指数和地表温度等预测因子，以夜间分区方法估算GPP，为全球生态系统生产力研究提供基准数据。