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.
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