Lin Shangrong, Huang Xiaojuan, Wang Caiqun, He Tao, Zhang Xiao, Shen Ruoque, Peng Qiongyan, Chen Xiuzhi, Zheng Yi, Dong Jie, Liang Shunlin, Yuan Wenping
Carbon-Water Research Station in Karst Regions of Northern Guangdong, School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510006, China.
International Research Center of Big Data for Sustainable Development Goals, Beijing, China.
Sci Data. 2024 Oct 1;11(1):1065. doi: 10.1038/s41597-024-03893-x.
Estimating gross primary production (GPP) of terrestrial ecosystems is important for understanding the terrestrial carbon cycle. However, existed nationwide GPP datasets are primarily driven by coarse spatial resolutions (≥500 m) remotely sensed data, which fails to capture the spatial heterogeneity of GPP across different ecosystem types at land surface. This paper introduces a new GPP dataset, Hi-GLASS GPP v1, with a fine spatial resolution (30-m) and monthly temporal resolution from 2016 to 2020 in China. The Hi-GLASS GPP v1 dataset is generated from 30-m Landsat data using a process based light use efficiency model. The Hi-GLASS GPP v1 model integrates a detailed map of maize plantations, a crucial C4 crop in China known for its higher photosynthetic efficiency compared to C3 crops. This inclusion helps correct the underestimation of GPP that typically occurs when all croplands are categorized as C3. The Hi-GLASS GPP v1 dataset demonstrates a robust correlation with GPP data derived from eddy covariance towers, thereby enabling a more accurate assessment of terrestrial carbon sequestration across China.
估算陆地生态系统的总初级生产力（GPP）对于理解陆地碳循环至关重要。然而，现有的全国性GPP数据集主要由空间分辨率较粗（≥500米）的遥感数据驱动，无法捕捉陆地表面不同生态系统类型间GPP的空间异质性。本文介绍了一个新的GPP数据集，即Hi-GLASS GPP v1，其空间分辨率精细（30米），时间分辨率为2016年至2020年的月度数据，覆盖中国范围。Hi-GLASS GPP v1数据集是利用基于过程的光能利用效率模型，从30米分辨率的陆地卫星数据生成的。Hi-GLASS GPP v1模型整合了玉米种植园的详细地图，玉米是中国一种关键的C4作物，与C3作物相比，其光合效率更高。这一纳入有助于纠正当所有农田都归类为C3时通常出现的GPP低估问题。Hi-GLASS GPP v1数据集与来自涡度协方差塔的GPP数据显示出很强的相关性，从而能够更准确地评估中国陆地的碳固存情况。
