<strong>Reducing spatial resolution increased net primary productivity prediction of terrestrial ecosystems: A Random Forest approach</strong>

Net primary production (NPP) is a pivotal component of the terrestrial carbon dynamic, as it directly contributes to the sequestration of atmospheric carbon by vegetation. However, significant variations and uncertainties persist in both the total amount and spatiotemporal patterns of terrestrial NPP, primarily stemming from discrepancies among datasets, modeling approaches, and spatial resolutions. In order to assess the influence of different spatial resolutions on global NPP, we employed a random forest (RF) model using a comprehensive global observational dataset to predict NPP at 0.05°, 0.25°, and 0.5° resolutions. Our dataset contained two types of data: field observations of global NPP and predicted NPP at three resolutions. The relevant details of the data files are as follows:Filename: 02_NPP_1981_2018_0.05_degree.nc; 03_NPP_1981_2018_0.25_degree.nc; 04_NPP_1981_2018_0.5_degree.ncFile information：‘02_NPP_1981_2018_0.05_degree.nc’ is the predicted global NPP data at 0.05° spatial resolution; ‘03_NPP_1981_2018_0.25_degree.nc’ is the predicted global NPP data at 0.25° spatial resolution; ‘04_NPP_1981_2018_0.5_degree.nc’ is the predicted global NPP data at 0.5° spatial resolution.The unit for these data is 'g C m^-2a^-1'.<b>Corresponding author</b>: Xiaolu Tang ([e-mail])<b>Acknowledgments</b>:The study was supported by the National Science Foundation of China (32271856); the Everest Scientific Research Program, Chengdu University of Technology (80000-2021ZF11410) and Large Engineering Project of Huaneng, China (JC2022/D01).