A global hourly gross primary production dataset from 2001 to 2020

作者：Yong Wang, Zhi He, Wei Zhao, Gaofei Yin, Xiaobin Guan, Xinyao Xie · 发表于：Scientific Data · 年份：2025 · DOI：10.1038/s41597-025-06371-0 · 被引用次数：2 · 研究领域：Remote Sensing in Agriculture、Plant Water Relations and Carbon Dynamics、Atmospheric and Environmental Gas Dynamics

Fine-resolution estimation of gross primary production (GPP) is essential for advancing our knowledge of ecosystem carbon cycling. However, most existing global GPP products are constrained by coarse temporal resolutions (typically ≥ 8 days), limiting their capacity to capture short-term variations in ecosystem productivity. This paper presents a global new GPP dataset for 2001–2020, based on a modified radiation scalar two-leaf LUE (RTL-LUE) model. The RTL-LUE GPP dataset is generated at an hourly temporal resolution and a spatial resolution of 0.1°, driven by the hourly climate data from ERA5-land, GLASS leaf area index, MODIS land cover, and NOAA atmospheric CO 2 concentration. During 2001–2020, this dataset provides a slightly lower global total GPP (124.77 PgC/yr) than the 8-day TL-LUE GPP dataset (126.92 PgC/yr), primarily due to its ability to capture short-term extreme stresses more effectively. Notably, this dataset reveals that annual GPP variability can reach up to approximately 0.10 g C/m 2 /h at hourly scales. The RTL-LUE GPP demonstrates robust performance at 184 towers, thereby improving insights into the temporal dynamics of carbon fluxes.