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Annual gross primary productivity (AGPP) is the basis for grain production and terrestrial carbon sequestration. Mapping regional AGPP from site measurements provides methodological support for analysing AGPP spatiotemporal variations thereby ensures regional food security and mitigates climate change. Based on 641 site-year eddy covariance measuring AGPP from China, we built an AGPP mapping scheme based on its formation and selected the optimal mapping way, which was conducted through analysing the predicting performances of divergent mapping tools, variable combinations, and mapping approaches in predicting observed AGPP variations. The reasonability of the selected optimal scheme was confirmed by assessing the consistency between its generating AGPP and previous products in spatiotemporal variations and total amount. Random forest regression tree explained 85 % of observed AGPP variations, outperforming other machine learning algorithms and classical statistical methods. Variable combinations containing climate, soil, and biological factors showed superior performance to other variable combinations. Mapping AGPP through predicting AGPP per leaf area (PAGPP) explained 86 % of AGPP variations, which was superior to other approaches. The optimal scheme was thus using a random forest regression tree, combining climate, soil, and biological variables, and predicting PAGPP. The optimal scheme generating AGPP of Chinese terrestrial ecosystems decreased from southeast to northwest, which was highly consistent with previous products. The interannual trend and interannual variation of our generating AGPP showed a decreasing trend from east to west and from southeast to northwest, respectively, which was consistent with data-oriented products. The mean total amount of generated AGPP was 7.03 ± 0.45 PgC yr -1 falling into the range of previous works. Considering the consistency between the generated AGPP and previous products, our optimal mapping way was suitable for mapping AGPP from site measurements. Our results provided a methodological support for mapping regional AGPP and other fluxes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Annual gross primary productivity (AGPP) serves as the basis for forming biomass and carbon sinks. Analysing the effects of ecosystem types on AGPP spatial variations would be beneficial for clarifying the spatial variability in AGPP, which would serve ecological management practices such as ensuring regional food security. Based on published eddy covariance measurements in China, we collected AGPP data from 128 ecosystems and analysed the effects of ecosystem types on the spatial variations in AGPP to reveal the AGPP spatial variability and its influencing factors over terrestrial ecosystems of China. The results showed that AGPP significantly differed among ecosystem types and vegetation regions, with the lowest AGPP appearing in grasslands, while different ecosystem types had comparable AGPP within the same vegetation region. The AGPP of all ecosystem types showed a decreasing latitudinal trend but slight longitudinal and altitudinal trends. Mean annual air temperature (MAT) and mean annual precipitation (MAP) were found to affect the spatial variations in AGPP over most ecosystem types, while other factors played little role. The mean annual leaf area index (LAI) and the maximum leaf area index (MLAI) were also found to affect the spatial variations in AGPP over most ecosystem types. Factors influencing the AGPP spatial variations differed among ecosystem types, but all included climatic and biotic factors. Therefore, climate inducing spatial distribution of ecosystem types and the non-zonal water supply made AGPP values and factors affecting the spatial variations in AGPP differ among ecosystem types, while different ecosystem types within the same vegetation region had comparable AGPP values. The spatial variation in AGPP over terrestrial ecosystems of China resulted from the integrated effects of climatic and biotic factors. Our study provided data support for improving the understanding of global AGPP spatial variability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)