A dataset of annual gross primary productivity in China’s terrestrial ecosystems during 2000-2020

Abstract

The annual gross primary productivity (AGPP) is the basis of food production and carbon sequestration in terrestrial ecosystems. An accurate assessment of regional AGPP can provide a theoretical basis for analyzing the spatiotemporal variation of AGPP and ensuring regional food security and mitigating climate change trends. Based on Chinese Flux Observation and Research Network (ChinaFLUX) measurements and public datasets, we produced a dataset of annual gross primary productivity over China’s terrestrial ecosystems was constructed. In combination with biological, climatic, and soil factors, we used the random forest regression tree to construct the assessment model of China AGPP by simulating the AGPP of unit leaf area. The dataset of annual gross primary productivity over China’s terrestrial ecosystems during 2000-2020 was generated with a spatial resolution of 30arcsecond and a data format of tiff. The dataset can provide validation data for model simulation, as well as data support for regional productivity, ecological quality, and assessment and management of terrestrial carbon sinks.

Spatio-temporal analysis of the impact of land urbanization on the gross primary productivity of vegetation in the Middle Reaches of the Yangtze River Urban Agglomeration: new evidence from the township scale

“…Internationally recognized methods for estimating GPP include continuous observation at flux stations, estimation by terrestrial ecological process models, and other methods. In this study, we used the GPP dataset provided by Fan et al (2023) in China Scientific Data Platform. This dataset is based on the long-term networked observation data and open datasets of ChinaFLUX, combining biological, climatic and soil factors, and using the random forest regression tree model to simulate GPP per unit of leaf area to construct the GPP dataset of China from 2000 to 2020, with a spatial resolution of 30 arcsec (Dataset doi: 10.57760/ sciencedb.o00119.00077).…”