In The Science of the total environment
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.
Zhu Xian-Jin, Yu Gui-Rui, Chen Zhi, Zhang Wei-Kang, Han Lang, Wang Qiu-Feng, Chen Shi-Ping, Liu Shao-Min, Wang Hui-Min, Yan Jun-Hua, Tan Jun-Lei, Zhang Fa-Wei, Zhao Feng-Hua, Li Ying-Nian, Zhang Yi-Ping, Shi Pei-Li, Zhu Jiao-Jun, Wu Jia-Bing, Zhao Zhong-Hui, Hao Yan-Bin, Sha Li-Qing, Zhang Yu-Cui, Jiang Shi-Cheng, Gu Feng-Xue, Wu Zhi-Xiang, Zhang Yang-Jian, Zhou Li, Tang Ya-Kun, Jia Bing-Rui, Li Yu-Qiang, Song Qing-Hai, Dong Gang, Gao Yan-Hong, Jiang Zheng-De, Sun Dan, Wang Jian-Lin, He Qi-Hua, Li Xin-Hu, Wang Fei, Wei Wen-Xue, Deng Zheng-Miao, Hao Xiang-Xiang, Li Yan, Liu Xiao-Li, Zhang Xi-Feng, Zhu Zhi-Lin
2022-Oct-12
Carbon cycle, Climate change, Eddy covariance, Machine learning, Scale extension, Terrestrial ecosystem
