Abstract: Annual net ecosystem productivity (NEP), the amount of net carbon sequestration during a year, serves as the basis of terrestrial carbon sink. Quantifying the spatial variations of NEP and its trend would enhance our understandings on the response and adaption of ecosystems to environmental change, which also serves for the regional carbon management targeting at carbon neutrality. Based on process-based model and data-driven model simulating NEP, we selected the optimal simulating NEP mostly representing NEP spatial variations with multiple site eddy covariance measurements to develop the spatial downscaling method and generate high resolution NEP data of China, which was used to examine the spatial variations of NEP and its trend and driving factors during 2000-2017. Compared with process-based model results, data-driven model simulating NEP could mostly represent the spatial variation of site measurements. The random forest regression based on climate, soil, and biological data combining with the simple scaling could successfully downscale NEP to a high spatial resolution. From 2000 to 2017, the total amount of NEP in China was (1.30±0.03) Pg C·a^-1, showing a decreasing-increasing pattern with the inflection point in 2009. Chinese NEP decreased from southeast to northwest, showing a descending latitudinal distribution and an ascending longitudinal distribution, with the combined effects of climate and biotic factors. NEP trend decreased from east towards west, which was only accompanied with a slightly ascending longitudinal distribution, while photosynthetically active radiation and soil organic carbon content dominated the spatial variations of NEP trend. Therefore, the spatial patterns of generated NEP obviously differed from those of NEP trend, suggesting the obvious difference between the responses and adaptions of ecosystems to environmental changes.

Key words: eddy covariance, terrestrial ecosystem, carbon neutrality, spatiotemporal variation, spatial downscaling